From AI pilots to public decisions: what it really takes to close the intelligence gap
Across the public sector, the conversation about AI has shifted. The question is no longer whether AI can generate insight—most leaders have already seen impressive pilots. The harder question is whether those insights survive the realities of government: public scrutiny, auditability, cross‑department delivery, and the need to explain decisions in plain language. That challenge was recently articulated by Sadaf Mozaffarian, writing in Smart Cities World, in the context of city‑scale AI deployments. Governments don’t need more experiments. They need decision‑ready intelligence—intelligence that can be acted on safely, governed consistently, and defended when outcomes are questioned. What’s emerging now is a more operational lens on AI adoption, one that exposes two issues many pilots quietly avoid. Decision latency is the real enemy In government, decision latency is not about slow analytics, it’s the time lost between having a signal and being able to act on it with confidence. Much of the focus in AI discussions is on accuracy, bias, or model performance. But in cities, the more damaging problem is often this latency. When data is fragmented across departments, policies live in PDFs, and institutional knowledge walks out the door at 5pm, leaders may have insight but still can’t decide fast enough. AI pilots often demonstrate answers in isolation, but they don’t reduce the friction between insight, approval, and execution. Decision‑ready intelligence directly attacks this problem. It brings together: Operational data already trusted by the organization Policy and regulatory context that constrains decisions Human checkpoints that reflect how accountability actually works The result isn’t faster answers—it’s faster decisions that stick, because they align with how governments are structured to operate. Institutional memory is infrastructure Cities invest heavily in physical infrastructure—roads, pipes, facilities—but far less deliberately in institutional memory. Yet planning rationales, inspection notes, precedent cases, and prior decisions are often what make or break today’s choices. Consider a routine enforcement or permitting decision that looks reasonable on current data, but quietly contradicts a prior settlement, a regulator’s interpretation, or a lesson learned during a past inquiry. AI systems that don’t account for this history don’t just miss context, they create risk. Decision‑ready intelligence treats institutional memory as a first‑class asset. It ensures that when AI supports a decision, it does so with: Access to relevant historical records and prior outcomes Clear lineage back to source documents and policies Logging that preserves not just what was decided, but why This is what allows governments to move faster without relearning the same lessons under audit pressure. Why this matters now Public sector AI initiatives rarely fail because of a lack of ambition. They stall because trust questions—governance, records, explainability—arrive too late. By the time leaders ask, “Can we stand behind this decision?” the system was never designed to answer. Decision‑ready intelligence flips that sequence. Governance is not bolted on after the pilot; it’s built into the operating model from the start. That’s what allows agencies to scale from a single use case to repeatable patterns across departments. A practical starting point The cities making progress aren’t trying to transform everything at once. They start small but visible: Identify one cross‑department “moment of truth” Define what must be logged, retained, and explainable Connect just enough data, policy, and work context to support that decision From there, they reuse the same patterns—governed data products, policy knowledge bases, and human‑in‑the‑loop workflows—to scale responsibly. AI in government will ultimately be judged the same way every public investment is judged: by outcomes, fairness, and public confidence. Closing the intelligence gap isn’t about smarter models. It’s about designing decision systems that reflect how governments actually work—and are held accountable. Learn more by reading Sadaf's full article: Closing the intelligence gap: how cities turn AI experiments into operational impact
SharePoint at 25: The knowledge platform for Copilot and agents
Join us for a global digital event to celebrate the 25th birthday of SharePoint! Gear up for an exciting look at SharePoint’s historic moments along with an exclusive look ahead to the next chapter of SharePoint’s AI future! You will discover how SharePoint’s new content AI capabilities and intelligent experiences will transform the way people create, manage, and collaborate. Be sure to stick around after for our live Ask Microsoft Anything (AMA), where you can ask your questions about the exciting new SharePoint features directly to the product team! 🛠️ Don’t miss the SharePoint Hackathon in March 2026 Design, create, share! We are excited to invite you to a hackathon dedicated to crafting exceptional employee experiences using AI and the latest SharePoint features. More details coming soon. Event LinkBuilding a Dual Sidecar Pod: Combining GitHub Copilot SDK with Skill Server on Kubernetes
Why the Sidecar Pattern? In Kubernetes, a Pod is the smallest deployable unit — a single Pod can contain multiple containers that share the same network namespace and storage volumes. The Sidecar pattern places auxiliary containers alongside the main application container within the same Pod. These Sidecar containers extend or enhance the main container's functionality without modifying it. 💡 Beginner Tip: If you're new to Kubernetes, think of a Pod as a shared office — everyone in the room (containers) has their own desk (process), but they share the same network (IP address), the same file cabinet (storage volumes), and can communicate without leaving the room (localhost communication). The Sidecar pattern is not a new concept. As early as 2015, the official Kubernetes blog described this pattern in a post about Composite Containers. Service mesh projects like Envoy, Istio, and Linkerd extensively use Sidecar containers for traffic management, observability, and security policies. In the AI application space, we are now exploring how to apply this proven pattern to new scenarios. Why does this matter? There are three fundamental reasons: 1. Separation of Concerns Each container in a Pod has a single, well-defined responsibility. The main application container doesn't need to know how AI content is generated or how skills are managed — it only serves the results. This separation allows each component to be independently tested, debugged, and replaced, aligning with the Unix philosophy of "do one thing well." In practice, this means: the frontend team can iterate on Nginx configuration without affecting AI logic; AI engineers can upgrade the Copilot SDK version without touching skill management code; and operations staff can adjust skill configurations without notifying the development team. 2. Shared Localhost Network All containers in a Pod share the same network namespace, with the same 127.0.0.1. This means communication between Sidecars is just a simple localhost HTTP call — no service discovery, no DNS resolution, no cross-node network hops. From a performance perspective, localhost communication traverses the kernel's loopback interface, with latency typically in the microsecond range. In contrast, cross-Pod ClusterIP Service calls require routing through kube-proxy's iptables/IPVS rules, with latency typically in the millisecond range. For AI agent scenarios that require frequent interaction, this difference is meaningful. From a security perspective, localhost communication doesn't traverse any network interface, making it inherently immune to eavesdropping by other Pods in the cluster. Unless a Service is explicitly configured, Sidecar ports are not exposed outside the Pod. 3. Efficient Data Transfer via Shared Volumes Kubernetes emptyDir volumes allow containers within the same Pod to share files on disk. Once a Sidecar writes a file, the main container can immediately read and serve it — no message queues, no additional API calls, no databases. This is ideal for workflows where one container produces artifacts (such as generated blog posts) and another consumes them. ⚠️ Technical Precision Note: "Efficient" here means eliminating the overhead of network serialization/deserialization and message middleware. However, emptyDir fundamentally relies on standard file system I/O (disk read/write or tmpfs) and is not equivalent to OS-level "Zero-Copy" (such as the sendfile() system call or DMA direct memory access). For blog content generation — a file-level data transfer use case — filesystem sharing is already highly efficient and sufficiently simple. In the gh-cli-blog-agent project, we take this pattern to its fullest extent by using two Sidecars within a single Pod: A Note on Kubernetes Native Sidecar Containers It is worth noting that Kubernetes 1.28 (August 2023) introduced native Sidecar container support via KEP-753, which reached GA (General Availability) in Kubernetes 1.33 (April 2025). Native Sidecars are implemented by setting restartPolicy: Always on initContainers, providing capabilities that the traditional approach lacks: Deterministic startup order: init containers start in declaration order; main containers only start after Sidecar containers are ready Non-blocking Pod termination: Sidecars are automatically cleaned up after main containers exit, preventing Jobs/CronJobs from being stuck Probe support: Sidecars can be configured with startup, readiness, and liveness probes to signal their operational state This project currently uses the traditional approach of deploying Sidecars as regular containers, with application-level health check polling (wait_for_skill_server) to handle startup dependencies. This approach is compatible with all Kubernetes versions (1.24+), making it suitable for scenarios requiring broad compatibility. If your cluster version is ≥ 1.29 (or ≥ 1.33 for GA stability), we strongly recommend migrating to native Sidecars for platform-level startup order guarantees and more graceful lifecycle management. Migration example: # Native Sidecar syntax (Kubernetes 1.29+) initContainers: - name: skill-server image: blog-agent-skill restartPolicy: Always # Key: marks this as a Sidecar ports: - containerPort: 8002 startupProbe: # Platform-level startup readiness signal httpGet: path: /health port: 8002 periodSeconds: 2 failureThreshold: 30 - name: copilot-agent image: blog-agent-copilot restartPolicy: Always ports: - containerPort: 8001 containers: - name: blog-app # Main container starts last; Sidecars are ready image: blog-agent-main ports: - containerPort: 80 Architecture Overview The deployment defines three containers and three volumes: Container Image Port Role blog-app blog-agent-main 80 Nginx — serves Web UI and reverse proxies to Sidecars copilot-agent blog-agent-copilot 8001 FastAPI — AI blog generation powered by GitHub Copilot SDK skill-server blog-agent-skill 8002 FastAPI — skill file management and synchronization Volume Type Purpose blog-data emptyDir Copilot agent writes generated blogs; Nginx serves them skills-shared emptyDir Skill server writes skill files; Copilot agent reads them skills-source ConfigMap Kubernetes-managed skill definition files (read-only) 💡 Design Insight: The three-volume design embodies the "least privilege" principle — blog-data is shared only between the Copilot agent (write) and Nginx (read); skills-shared is shared only between the skill server (write) and the Copilot agent (read). skills-source provides read-only skill definition sources via ConfigMap, forming a unidirectional data flow: ConfigMap → skill-server → shared volume → copilot-agent. The Kubernetes deployment YAML clearly describes this structure: volumes: - name: blog-data emptyDir: sizeLimit: 256Mi # Production best practice: always set sizeLimit to prevent disk exhaustion - name: skills-shared emptyDir: sizeLimit: 64Mi # Skill files are typically small - name: skills-source configMap: name: blog-agent-skill ⚠️ Production Recommendation: The original configuration used emptyDir: {} without a sizeLimit. In production, an unrestricted emptyDir can grow indefinitely until it exhausts the node's disk space, triggering a node-level DiskPressure condition and causing other Pods to be evicted. Always setting a reasonable sizeLimit for emptyDir is part of the Kubernetes security baseline. Community tools like Kyverno can enforce this practice at the cluster level. Nginx reverse proxies route requests to Sidecars via localhost: # Reverse proxy to copilot-agent sidecar (localhost:8001 within the same Pod) location /agent/ { proxy_pass http://127.0.0.1:8001/; proxy_set_header Host $host; proxy_set_header X-Request-ID $request_id; # Enables cross-container request tracing proxy_read_timeout 600s; # AI generation may take a while } # Reverse proxy to skill-server sidecar (localhost:8002 within the same Pod) location /skill/ { proxy_pass http://127.0.0.1:8002/; proxy_set_header Host $host; } Since all three containers share the same network namespace, 127.0.0.1:8001 and 127.0.0.1:8002 are directly accessible — no ClusterIP Service is needed for intra-Pod communication. This is a core feature of the Kubernetes Pod networking model: all containers within the same Pod share a single network namespace, including IP address and port space. Advantage 1: GitHub Copilot SDK as a Sidecar Encapsulating the GitHub Copilot SDK as a Sidecar, rather than embedding it in the main application, provides several architectural advantages. Understanding the GitHub Copilot SDK Architecture Before diving deeper, let's understand how the GitHub Copilot SDK works. The SDK entered technical preview in January 2026, exposing the production-grade agent runtime behind GitHub Copilot CLI as a programmable SDK supporting Python, TypeScript, Go, and .NET. The SDK's communication architecture is as follows: The SDK client communicates with a locally running Copilot CLI process via the JSON-RPC protocol. The CLI handles model routing, authentication management, MCP server integration, and other low-level details. This means you don't need to build your own planner, tool loop, and runtime — these are all provided by an engine that has been battle-tested in production at GitHub's scale. The benefit of encapsulating this SDK in a Sidecar container is: containerization isolates the CLI process's dependencies and runtime environment, preventing dependency conflicts with the main application or other components. Cross-Platform Node.js Installation in the Container A notable implementation detail is how Node.js (required by the Copilot CLI) is installed inside the container. Rather than relying on third-party APT repositories like NodeSource — which can introduce DNS resolution failures and GPG key management issues in restricted network environments — the Dockerfile downloads the official Node.js binary directly from nodejs.org with automatic architecture detection: # Install Node.js 20+ (official binary, no NodeSource APT repo needed) ARG NODE_VERSION=20.20.0 RUN DPKG_ARCH=$(dpkg --print-architecture) \ && case "${DPKG_ARCH}" in amd64) ARCH=x64;; arm64) ARCH=arm64;; armhf) ARCH=armv7l;; *) ARCH=${DPKG_ARCH};; esac \ && curl -fsSL "https://nodejs.org/dist/v${NODE_VERSION}/node-v${NODE_VERSION}-linux-${ARCH}.tar.xz" -o node.tar.xz \ && tar -xJf node.tar.xz -C /usr/local --strip-components=1 --no-same-owner \ && rm -f node.tar.xz The case statement maps Debian's architecture identifiers (amd64, arm64, armhf) to Node.js's naming convention (x64, arm64, armv7l). This ensures the same Dockerfile works seamlessly on both linux/amd64 (Intel/AMD) and linux/arm64 (Apple Silicon, AWS Graviton) build platforms — an important consideration given the growing adoption of ARM-based infrastructure. Independent Lifecycle and Resource Management The Copilot agent is the most resource-intensive component — it needs to run the Copilot CLI process, manage JSON-RPC communication, and handle streaming responses. By isolating it in its own container, we can assign dedicated CPU and memory limits without affecting the lightweight Nginx container: # copilot-agent: needs more resources for AI inference coordination resources: requests: cpu: 250m memory: 512Mi limits: cpu: "1" memory: 2Gi # blog-app: lightweight Nginx with minimal resource needs resources: requests: cpu: 50m memory: 64Mi limits: cpu: 200m memory: 128Mi This resource isolation delivers two key benefits: Fault isolation: If the Copilot agent crashes due to a timeout or memory spike (OOMKilled), Kubernetes only restarts that container — the Nginx frontend continues running and serving previously generated content. Users see "generation feature temporarily unavailable" rather than "entire site is down." Fine-grained resource scheduling: The Kubernetes scheduler selects nodes based on the sum of Pod-level resource requests. Distributing resource requests across containers allows kubelet to more precisely track each component's actual resource consumption, helping HPA (Horizontal Pod Autoscaler) make better scaling decisions. Graceful Startup Coordination In a multi-Sidecar Pod, regular containers start concurrently (note: this is precisely one of the issues that native Sidecars, discussed earlier, can solve). The Copilot agent handles this through application-level startup dependency checks — it waits for the skill server to become healthy before initializing the CopilotClient: async def wait_for_skill_server(url: str, retries: int = 30, delay: float = 2.0): """Wait for the skill-server sidecar to become healthy. In traditional Sidecar deployments (regular containers), containers start concurrently with no guaranteed startup order. This function implements application-level readiness waiting. If using Kubernetes native Sidecars (initContainers + restartPolicy: Always), the platform guarantees Sidecars start before main containers, which can simplify this logic. """ async with httpx.AsyncClient() as client: for i in range(retries): try: resp = await client.get(f"{url}/health", timeout=5.0) if resp.status_code == 200: logger.info(f"Skill server is healthy at {url}") return True except Exception: pass logger.info(f"Waiting for skill server... ({i + 1}/{retries})") await asyncio.sleep(delay) raise RuntimeError(f"Skill server at {url} did not become healthy") This pattern is critical in traditional Sidecar architectures: you cannot assume startup order, so explicit readiness checks are necessary. The wait_for_skill_server function polls http://127.0.0.1:8002/health at 2-second intervals up to 30 times (maximum total wait of 60 seconds) — simple, effective, and resilient. 💡 Comparison: With native Sidecars, the skill-server would be declared as an initContainer with a startupProbe. Kubernetes would ensure the skill-server is ready before starting the copilot-agent. In that case, wait_for_skill_server could be simplified to a single health check confirmation rather than a retry loop. SDK Configuration via Environment Variables All Copilot SDK configuration is passed through Kubernetes-native primitives, reflecting the 12-Factor App principle of externalized configuration: env: - name: SKILL_SERVER_URL value: "http://127.0.0.1:8002" - name: SKILLS_DIR value: "/skills-shared/blog/SKILL.md" - name: COPILOT_GITHUB_TOKEN valueFrom: secretKeyRef: name: blog-agent-secret key: copilot-github-token Key design decisions explained: COPILOT_GITHUB_TOKEN is stored in a Kubernetes Secret — never baked into images or passed as build arguments. Using the GitHub Copilot SDK requires a valid GitHub Copilot subscription (unless using BYOK mode, i.e., Bring Your Own Key), making secure management of this token critical. SKILLS_DIR points to skill files synchronized to a shared volume by the other Sidecar. This means the Copilot agent container image is completely stateless and can be reused across different skill configurations. SKILL_SERVER_URL uses 127.0.0.1 instead of a service name — since this is intra-Pod communication, DNS resolution is unnecessary. 🔐 Production Security Tip: For stricter security requirements, consider using External Secrets Operator to sync Secrets from AWS Secrets Manager, Azure Key Vault, or HashiCorp Vault, rather than managing them directly in Kubernetes. Native Kubernetes Secrets are only Base64-encoded by default, not encrypted at rest (unless Encryption at Rest is enabled). CopilotClient Sessions and Skill Integration The core of the Copilot Sidecar lies in how it creates sessions with skill directories. When a blog generation request is received, it creates a session with access to skill definitions: session = await copilot_client.create_session({ "model": "claude-sonnet-4-5-20250929", "streaming": True, "skill_directories": [SKILLS_DIR] }) The skill_directories parameter points to files on the shared volume — files placed there by the skill-server sidecar. This is the handoff point: the skill server manages which skills are available, and the Copilot agent consumes them. Neither container needs to know about the other's internal implementation — they are coupled only through the filesystem as an implicit contract. 💡 About Copilot SDK Skills: The GitHub Copilot SDK allows you to define custom Agents, Skills, and Tools. Skills are essentially instruction sets written in Markdown format (typically named SKILL.md) that define the agent's behavior, constraints, and workflows in a specific domain. This is consistent with the .copilot_skills/ directory mechanism in GitHub Copilot CLI. File-Based Output to Shared Volumes Generated blog posts are written to the blog-data shared volume, which is simultaneously mounted in the Nginx container: BLOG_DIR = os.path.join(WORK_DIR, "blog") # ... # Blog saved as blog-YYYY-MM-DD.md # Nginx can serve it immediately from /blog/ without any restart The Nginx configuration auto-indexes this directory: location /blog/ { alias /usr/share/nginx/html/blog/; autoindex on; } The moment the Copilot agent writes a file, it's immediately accessible through the Nginx Web UI. No API calls, no database writes, no cache invalidation — just a shared filesystem. This file-based data transfer has an additional benefit: natural persistence and auditability. Each blog exists as an independent Markdown file with a date-timestamp in its name, making it easy to trace generation history. (Note, however, that emptyDir lifecycle is tied to the Pod — data is lost when the Pod is recreated. For persistence needs, see the "Production Recommendations" section below.) Advantage 2: Skill Server as a Sidecar The skill server is the second Sidecar — a lightweight FastAPI service responsible for managing the skill definitions used by the Copilot agent. Separating skill management into its own container offers clear advantages. Decoupled Skill Lifecycle Skill definitions are stored in a Kubernetes ConfigMap: apiVersion: v1 kind: ConfigMap metadata: name: blog-agent-skill data: SKILL.md: | # Blog Generator Skill Instructions You are a professional technical evangelist... ## Key Requirements 1. Outline generation 2. Mandatory online research (DeepSearch) 3. Technical evangelist perspective ... ConfigMaps can be updated independently of any container image. When you run kubectl apply to update a ConfigMap, Kubernetes synchronizes the change to the volumes mounted in the Pod. ⚠️ Important Detail: ConfigMap volume updates do not take effect immediately. The kubelet detects ConfigMap changes through periodic synchronization, with a default sync period controlled by --sync-frequency (default: 1 minute), plus the ConfigMap cache TTL. The actual propagation delay can be 1–2 minutes. If immediate effect is needed, you must actively call the /sync endpoint to trigger a file synchronization: def sync_skills(): """Copy skill files from ConfigMap source to the shared volume.""" source = Path(SKILLS_SOURCE_DIR) dest = Path(SKILLS_SHARED_DIR) / "blog" dest.mkdir(parents=True, exist_ok=True) synced = 0 for skill_file in source.iterdir(): if skill_file.is_file(): target = dest / skill_file.name shutil.copy2(str(skill_file), str(target)) synced += 1 return synced This design means: updating AI behavior requires no container image rebuilds or redeployments. You simply update the ConfigMap, trigger a sync, and the agent's behavior changes. This is a tremendous operational advantage for iterating on prompts and skills in production. 💡 Advanced Thought: Why not mount the ConfigMap directly to the copilot-agent's SKILLS_DIR path? While technically feasible, introducing the skill-server as an intermediary provides the triple value of validation, API access, and extensibility (see "Why Not Embed Skills in the Copilot Agent" below). Minimal Resource Footprint The skill server does one thing — serve and sync files. Its resource requirements reflect this: resources: requests: cpu: 50m memory: 64Mi limits: cpu: 200m memory: 256Mi Compared to the Copilot agent's 2Gi memory limit, the skill server costs a fraction of the resources. This is the beauty of the Sidecar pattern — you can add lightweight containers for auxiliary functionality without significantly increasing the Pod's total resource consumption. REST API for Skill Introspection The skill server provides a simple REST API that allows external systems or operators to query available skills: .get("/skills") async def list_skills(): """List all available skills.""" source = Path(SKILLS_SOURCE_DIR) skills = [] for f in sorted(source.iterdir()): if f.is_file(): skills.append({ "name": f.stem, "filename": f.name, "size": f.stat().st_size, "url": f"/skill/{f.name}", }) return {"skills": skills, "total": len(skills)} @app.get("/skill/{filename}") async def get_skill(filename: str): """Get skill content by filename.""" file_path = Path(SKILLS_SOURCE_DIR) / filename if not file_path.exists() or not file_path.is_file(): raise HTTPException(status_code=404, detail=f"Skill '{filename}' not found") return {"filename": filename, "content": file_path.read_text(encoding="utf-8")} This API serves multiple purposes: Debugging: Verify which skills are currently loaded without needing to kubectl exec into the container, significantly lowering the troubleshooting barrier. Monitoring: External tools can poll /skills to ensure the expected skill set is deployed. Combined with Prometheus Blackbox Exporter, you can implement configuration drift detection. Extensibility: Future systems can dynamically register or update skills via the API, providing a foundation for A/B testing different prompt strategies. Why Not Embed Skills in the Copilot Agent? Mounting the ConfigMap directly into the Copilot agent container seems simpler. But separating it into a dedicated Sidecar has the following advantages: Validation layer: The skill server can validate skill file format and content before synchronization, preventing invalid skill definitions from causing Copilot SDK runtime errors. API access: Skills become queryable and manageable through a REST interface, supporting operational automation. Independent evolution of logic: If skill management becomes more complex (e.g., dynamic skill registration, version management, prompt A/B testing, role-based skill distribution), the skill server can evolve independently without affecting the Copilot agent. Clear data flow: ConfigMap → skill-server → shared volume → copilot-agent. Each arrow is an explicit, observable step. When something goes wrong, you can pinpoint exactly which stage failed. 💡 Architectural Trade-off: For small-scale deployments or PoC (Proof of Concept) work, directly mounting the ConfigMap to the Copilot agent is a perfectly reasonable choice — fewer components means lower operational overhead. The Sidecar approach's value becomes fully apparent in medium-to-large-scale production environments. Architectural decisions should always align with team size, operational maturity, and business requirements. End-to-End Workflow Here is the complete data flow when a user requests a blog post generation: Every step uses intra-Pod communication — localhost HTTP calls or shared filesystem reads. No external network calls are needed between components. The only external dependency is the Copilot SDK's connection to GitHub authentication services and AI model endpoints via the Copilot CLI. The Kubernetes Service exposes three ports for external access: ports: - name: http # Nginx UI + reverse proxy port: 80 nodePort: 30081 - name: agent-api # Direct access to Copilot Agent port: 8001 nodePort: 30082 - name: skill-api # Direct access to Skill Server port: 8002 nodePort: 30083 ⚠️ Security Warning: In production, it is not recommended to directly expose the agent-api and skill-api ports via NodePort. These two APIs should only be accessible through the Nginx reverse proxy (/agent/ and /skill/ paths), with authentication and rate limiting configured at the Nginx layer. Directly exposing Sidecar ports bypasses the reverse proxy's security controls. Recommended configuration: # Production recommended: only expose the Nginx port ports: - name: http port: 80 targetPort: 80 # Combine with NetworkPolicy to restrict inter-Pod communication Production Recommendations and Architecture Extensions When moving this architecture from a development/demo environment to production, the following areas deserve attention: Cross-Platform Build and Deployment The project's Makefile auto-detects the host architecture to select the appropriate Docker build platform, eliminating the need for manual configuration: ARCH := $(shell uname -m) ifeq ($(ARCH),x86_64) DOCKER_PLATFORM ?= linux/amd64 else ifeq ($(ARCH),aarch64) DOCKER_PLATFORM ?= linux/arm64 else ifeq ($(ARCH),arm64) DOCKER_PLATFORM ?= linux/arm64 else DOCKER_PLATFORM ?= linux/amd64 endif Both macOS and Linux are supported as development environments with dedicated tool installation targets: # macOS (via Homebrew) make install-tools-macos # Linux (downloads official binaries to /usr/local/bin) make install-tools-linux The Linux installation target downloads kubectl and kind binaries directly from upstream release URLs with architecture-aware selection, avoiding dependency on any package manager beyond curl and sudo. This makes the setup portable across different Linux distributions (Ubuntu, Debian, Fedora, etc.). Health Checks and Probe Configuration Configure complete probes for each container to ensure Kubernetes can properly manage container lifecycles: # copilot-agent probe example livenessProbe: httpGet: path: /health port: 8001 initialDelaySeconds: 10 periodSeconds: 30 timeoutSeconds: 5 readinessProbe: httpGet: path: /health port: 8001 periodSeconds: 10 startupProbe: # AI agent startup may be slow httpGet: path: /health port: 8001 periodSeconds: 5 failureThreshold: 30 # Allow up to 150 seconds for startup Data Persistence The emptyDir lifecycle is tied to the Pod. If generated blogs need to survive Pod recreation, consider these approaches: PersistentVolumeClaim (PVC): Replace the blog-data volume with a PVC; data persists independently of Pod lifecycle Object storage upload: After the Copilot agent generates a blog, asynchronously upload to S3/Azure Blob/GCS Git repository push: Automatically commit and push generated Markdown files to a Git repository for versioned management Security Hardening # Set security context for each container securityContext: runAsNonRoot: true runAsUser: 1000 readOnlyRootFilesystem: true # Only write through emptyDir allowPrivilegeEscalation: false capabilities: drop: ["ALL"] Observability Extensions The Sidecar pattern is naturally suited for adding observability components. You can add a third (or fourth) Sidecar to the same Pod for log collection, metrics export, or distributed tracing: Horizontal Scaling Strategy Since containers within a Pod scale together, HPA scaling granularity is at the Pod level. This means: If the Copilot agent is the bottleneck, scaling Pod replicas also scales Nginx and skill-server (minimal waste since they are lightweight) If skill management becomes compute-intensive in the future, consider splitting the skill-server from a Sidecar into an independent Deployment + ClusterIP Service for independent scaling Evolution Path from Sidecar to Microservices The dual Sidecar architecture provides a clear path for future migration to microservices: Each migration step only requires changing the communication method (localhost → Service DNS); business logic remains unchanged. This is the architectural flexibility that good separation of concerns provides. sample code - https://github.com/kinfey/Multi-AI-Agents-Cloud-Native/tree/main/code/GitHubCopilotSideCar Summary The dual Sidecar pattern in this project demonstrates a clean cloud-native AI application architecture: Main container (Nginx) stays lean and focused — it only serves HTML and proxies requests. It knows nothing about AI or skills. Sidecar 1 (Copilot Agent) encapsulates all AI logic. It uses the GitHub Copilot SDK, manages sessions, and generates content. Its only coupling to the rest of the Pod is through environment variables and shared volumes. The container image is built with cross-platform support — Node.js is installed from official binaries with automatic architecture detection, ensuring the same Dockerfile works on both amd64 and arm64 platforms. Sidecar 2 (Skill Server) provides a dedicated management layer for AI skill definitions. It bridges Kubernetes-native configuration (ConfigMap) with the Copilot SDK's runtime needs. This separation gives you independent deployability, isolated failure domains, and — most importantly — the ability to change AI behavior (skills, prompts, models) without rebuilding any container images. The Sidecar pattern is more than an architectural curiosity; it is a practical approach to composing AI services in Kubernetes, allowing each component to evolve at its own pace. With cross-platform build support (macOS and Linux, amd64 and arm64), Kubernetes native Sidecars reaching GA in 1.33, and AI development tools like the GitHub Copilot SDK maturing, we anticipate this "AI agent + Sidecar" combination pattern will see validation and adoption in more production environments. References GitHub Copilot SDK Repository — Official SDK supporting Python/TypeScript/Go/.NET KEP-753: Sidecar Containers — Kubernetes native Sidecar container proposal Kubernetes v1.33 Release: Sidecar Containers GA — Sidecar container GA announcement The Distributed System Toolkit: Patterns for Composite Containers — Classic early Kubernetes article on the Sidecar pattern 12-Factor App: Config — Externalized configuration principles
Getting Started with AI and MS Copilot - English
🚀 Ready to explore AI and Microsoft Copilot in a fun, hands-on way? Join our session: “Introduction to AI and Microsoft Copilot”—designed for educators who are just getting started! ✅ Learn the fundamentals of generative AI ✅ Master the art of creating effective prompts ✅ Discover practical ways to use these tools in your classroom ✅ Access ready-to-use teaching resources ✅ Practice with 10 interactive exercises 📅 Don’t miss this opportunity to boost your teaching with AI! #MicrosoftCopilot #AIinEducation #Educators #Innovation #TeachingTools Getting Started with AI and MS Copilot - English | Meeting-Join | Microsoft TeamsPartner Blog | Copilot monetization for SMBs: Start with Copilot Chat, scale with agents
This post kicks off a five-part series for Microsoft partners on the Copilot monetization opportunity for small and medium-sized businesses (SMBs). Each post follows a repeatable approach aligned to the Microsoft Customer Engagement Methodology (MCEM) and the Win Formula—from building credibility as Customer Zero to driving adoption and measurable outcomes, then extending value with agents and specializations. If you’re looking for practical ways to turn your customers’ AI interest into secure, scalable outcomes and repeatable revenue, you’re in the right place. SMBs want proof, not hype. They want an AI path that fits how they work today, stays governed, and delivers results. SMBs are the backbone of the global economy, accounting for 90% of all firms with around 400 million enterprises worldwide, according to the World Economic Forum’s SME Resource Hub. At the same time, 82% of leaders are rethinking core aspects of their strategy and operations, under constant pressure to do more with less. That combination is driving a shift from AI curiosity to AI decisions. Continue reading hereWhat Are the Best Practices for Implementing Microsoft Power Automate in a Growing Organization?
Hello Everyone, We’re planning to scale Microsoft Power Automate across our growing organization and would appreciate your expert insights. What are the best practices for implementing Microsoft Power Automate effectively — especially regarding governance, security, environment management, licensing, and user adoption? If you’ve implemented Microsoft Power Automate in a mid-sized or scaling company, please share: Key lessons learned Common mistakes to avoid Governance and CoE strategies Tips for managing citizen developers Looking forward to your valuable suggestions. Thank you!Unleashing the power of agents in Microsoft Planner
In today's fast-paced world, AI has become an essential tool for enhancing productivity and efficiency. We are committed to empowering our users with innovative solutions that simplify their work processes, and we’re thrilled to introduce the latest updates to Microsoft Planner, designed to leverage the power of Copilot and agents to streamline project management and task organization. With the recent announcement of Project Manager agent, rolling out in public preview in the Planner app in Microsoft Teams, and the rollout of the new Planner for the web, we are bringing you a comprehensive suite of tools to help you and your team achieve more with less effort. We invite you to explore these exciting new features and discover how they can transform the way you work. Introducing Project Manager agent Project Manager agent is a new AI-powered agent designed to enhance your planning experience by acting as a virtual project manager within your plans. Project Manager agent is built to streamline your planning process, empowering you to focus on the strategic aspects of your work while it handles some of the tasks on your behalf. It is the latest development in enhancing and transforming team collaboration with AI in Planner. Earlier this year, we introduced Copilot in Planner (preview) as a personal companion experience designed to work alongside your planner workflow. With Project Manager agent, we’re now bringing AI capabilities directly into your plans, allowing you to interact with the agent as an integral part of your plan. Project Manager agent takes your goals and automatically breaks them down into actionable tasks. But it doesn’t stop there, it can also execute these tasks on your behalf. By managing the plan and executing tasks, the agent enables you to focus on impactful decisions while it contributes directly to the success of your project. When you start a plan with Project Manager agent, it guides you to define a goal you want to achieve (for example, conducting research on a specific topic). The agent will then generate all the necessary tasks for the research topic. Assign these tasks to the agent, and it will execute on them, providing detailed output that is automatically captured in a Loop page embedded within each task. All members of the plan can collaborate directly within the Loop page, exchanging comments and feedback with the Project Manager agent. Upon selecting Regenerate, the agent incorporates the feedback and generates a refined response, improving the task outcomes. At any point, if Project Manager agent does not have adequate information to generate necessary output, it will even ask clarifying questions that will allow it to provide better responses. You will notice that Project Manager agent is capable of contributing at every step of your plan, delivering value throughout the process. This comes with a new Project Manager View in Planner—your hub for setting goals, generating tasks, and showcasing the execution status. This intuitive interface lets you set your project goals and generate tasks, assign tasks to team members or the agent for execution and track progress and statuses in real time. Additionally, in the board view, you can also group tasks by Project Manager, which shows all the Project manager tasks and status in the appropriate buckets. At its core, the Project Manager agent runs on the Multi-Agent Runtime Service (MARS), a platform built on Microsoft Autogen. MARS leverages specialized agents with unique expertise, enabling the Project Manager agent to perform effectively across diverse scenarios. See the blog post to learn more about how Project Manager agent and MARS function. To help you get started, we’ve provided predefined, customizable templates on various topics, allowing you to quickly kickstart a Project Manager plan and easily tailor it to meet your specific goals. Once you’ve selected a template, you can modify the plan to align with your specific needs and goals, ensuring it meets your unique requirements while leveraging the agent’s capabilities for streamlined execution. From idea, to plan, to done, the Project Manager agent is your trusted partner, ensuring every aspect of your plan is managed seamlessly. We’re also introducing the Microsoft Whiteboard canvas in Planner! This new feature allows you and your team to brainstorm directly within the context of your plan. Upon creating a new plan with the Project Manager agent, you will now see a Whiteboard tab in the plan. Whiteboard offers a dynamic and collaborative canvas within Planner, allowing users to easily convert ideas into tasks and streamline workflow from ideation to action. In the canvas, you and your team can engage in real-time collaboration using inking, sticky notes, and templates. With the Planner integration, you can quickly convert your notes to tasks in one click, directly adding them to your plan. We’re excited to bring these powerful tools to your planning experience, and we can’t wait to see the impact of the Project Manager agent in your daily workflows! The new Project Manager agent will be rolling out to public preview in the Planner app in Teams in the coming weeks. To explore these capabilities, customers are required to have a Microsoft 365 Copilot license and also need to ensure their current Microsoft 365 licensing allows them access to Microsoft Loop. As this is a preview release, please note that the features may evolve based on user feedback and ongoing improvements. Initially, Project Manager agent will support English language as the interaction medium, and other languages in the future. We’d love for you to try it out and share your thoughts to help shape its future development! Please select the thumbs up or thumbs down button in the Project Manager view or in the Task details to share what you think about the experience. In addition, we are announcing two more capabilities that will be coming soon to Microsoft Planner: 1. Copilot in My Tasks view: This feature brings AI-powered organization and prioritization to your tasks, helping users effectively manage their backlog and enabling them to stay on top of what matters most. 2. Automated status report emails: Provides the capability to automatically generate a status email from your plans, streamlining the process of sharing weekly updates so you can spend less time on emails and more time moving projects forward. We expect these features to be available for our customers to try early 2025. Join us at Microsoft Ignite to learn more about Project Manager agent in our breakout session, "Boost productivity with Copilot in Microsoft 365 apps." Try Planner for the web today! The new Planner for the web is now available! This marks another major milestone in the Planner journey that we announced last November at Ignite. In April, we launched the new Planner app in Microsoft Teams, and now we've completed the rollout with Planner for the web. Planner for the web now brings together the simplicity of Microsoft To Do, the collaboration of Planner, the power of Project for the web, and the intelligence of Microsoft 365 Copilot into a simple, familiar experience. Discover a new way to manage tasks for individual plans, team initiatives, and larger scale project management aligned to goals and key strategic objectives. We’re excited for you to try it out and share your thoughts. Thanks to your ongoing feedback, we’re continuing to roll out bug fixes and new enhancements regularly to both Planner in Teams and Planner for the Web. We have more exciting updates coming soon including the availability of Planner for the web in GCC, a new board view in the My Tasks view, an updated experience for the Planner app in Teams channels, and more. Check in regularly on the roadmap to learn about what’s coming. Explore the new Portfolios feature The frequently requested Portfolios feature is also rolling out in the Planner app in Teams and will start rolling out in the new Planner for the web app in the coming weeks! This powerful addition is designed to help you effortlessly manage and track progress across multiple plans. With Portfolios, you can now get a consolidated view of all your premium plans and tasks, ensuring nothing slips through the cracks. Whether you're coordinating between teams or looking for a top-down perspective, Portfolios in Planner makes it all possible in one location, streamlining workflows and enhancing collaboration. Join our session at Microsoft Ignite We are eager to share more details about these exciting updates during our session at Microsoft Ignite! Join us as we dive deeper into the new features and capabilities of Planner, and learn how they can elevate your teamwork. Don't miss this opportunity to connect with our team and get a firsthand look at what's new. Share your feedback Your feedback helps inform our feature updates and we look forward to hearing from you as you try out the new Planner! Provide feedback by using the Feedback button in the top right corner of the Planner app. We also encourage you to share any features you want to see in the app by adding it to our Planner Feedback Portal. Learn more Check out the recently refreshed Planner adoption page. Sign up to receive future communication about Planner. Check out the Microsoft 365 roadmap for feature descriptions and estimated release dates for Planner. Watch Planner demos for inspiration on how to get the most out of the new Planner app in Microsoft Teams. Watch the recording from September's What’s New and What’s Coming Next + AMA about the new Planner. Visit the Planner help page to learn more about the capabilities in the new Planner.Integrating Microsoft Foundry with OpenClaw: Step by Step Model Configuration
Step 1: Deploying Models on Microsoft Foundry Let us kick things off in the Azure portal. To get our OpenClaw agent thinking like a genius, we need to deploy our models in Microsoft Foundry. For this guide, we are going to focus on deploying gpt-5.2-codex on Microsoft Foundry with OpenClaw. Navigate to your AI Hub, head over to the model catalog, choose the model you wish to use with OpenClaw and hit deploy. Once your deployment is successful, head to the endpoints section. Important: Grab your Endpoint URL and your API Keys right now and save them in a secure note. We will need these exact values to connect OpenClaw in a few minutes. Step 2: Installing and Initializing OpenClaw Next up, we need to get OpenClaw running on your machine. Open up your terminal and run the official installation script: curl -fsSL https://openclaw.ai/install.sh | bash The wizard will walk you through a few prompts. Here is exactly how to answer them to link up with our Azure setup: First Page (Model Selection): Choose "Skip for now". Second Page (Provider): Select azure-openai-responses. Model Selection: Select gpt-5.2-codex , For now only the models listed (hosted on Microsoft Foundry) in the picture below are available to be used with OpenClaw. Follow the rest of the standard prompts to finish the initial setup. Step 3: Editing the OpenClaw Configuration File Now for the fun part. We need to manually configure OpenClaw to talk to Microsoft Foundry. Open your configuration file located at ~/.openclaw/openclaw.json in your favorite text editor. Replace the contents of the models and agents sections with the following code block: { "models": { "providers": { "azure-openai-responses": { "baseUrl": "https://<YOUR_RESOURCE_NAME>.openai.azure.com/openai/v1", "apiKey": "<YOUR_AZURE_OPENAI_API_KEY>", "api": "openai-responses", "authHeader": false, "headers": { "api-key": "<YOUR_AZURE_OPENAI_API_KEY>" }, "models": [ { "id": "gpt-5.2-codex", "name": "GPT-5.2-Codex (Azure)", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 400000, "maxTokens": 16384, "compat": { "supportsStore": false } }, { "id": "gpt-5.2", "name": "GPT-5.2 (Azure)", "reasoning": false, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 272000, "maxTokens": 16384, "compat": { "supportsStore": false } } ] } } }, "agents": { "defaults": { "model": { "primary": "azure-openai-responses/gpt-5.2-codex" }, "models": { "azure-openai-responses/gpt-5.2-codex": {} }, "workspace": "/home/<USERNAME>/.openclaw/workspace", "compaction": { "mode": "safeguard" }, "maxConcurrent": 4, "subagents": { "maxConcurrent": 8 } } } } You will notice a few placeholders in that JSON. Here is exactly what you need to swap out: Placeholder Variable What It Is Where to Find It <YOUR_RESOURCE_NAME> The unique name of your Azure OpenAI resource. Found in your Azure Portal under the Azure OpenAI resource overview. <YOUR_AZURE_OPENAI_API_KEY> The secret key required to authenticate your requests. Found in Microsoft Foundry under your project endpoints or Azure Portal keys section. <USERNAME> Your local computer's user profile name. Open your terminal and type whoami to find this. Step 4: Restart the Gateway After saving the configuration file, you must restart the OpenClaw gateway for the new Foundry settings to take effect. Run this simple command: openclaw gateway restart Configuration Notes & Deep Dive If you are curious about why we configured the JSON that way, here is a quick breakdown of the technical details. Authentication Differences Azure OpenAI uses the api-key HTTP header for authentication. This is entirely different from the standard OpenAI Authorization: Bearer header. Our configuration file addresses this in two ways: Setting "authHeader": false completely disables the default Bearer header. Adding "headers": { "api-key": "<key>" } forces OpenClaw to send the API key via Azure's native header format. Important Note: Your API key must appear in both the apiKey field AND the headers.api-key field within the JSON for this to work correctly. The Base URL Azure OpenAI's v1-compatible endpoint follows this specific format: https://<your_resource_name>.openai.azure.com/openai/v1 The beautiful thing about this v1 endpoint is that it is largely compatible with the standard OpenAI API and does not require you to manually pass an api-version query parameter. Model Compatibility Settings "compat": { "supportsStore": false } disables the store parameter since Azure OpenAI does not currently support it. "reasoning": true enables the thinking mode for GPT-5.2-Codex. This supports low, medium, high, and xhigh levels. "reasoning": false is set for GPT-5.2 because it is a standard, non-reasoning model. Model Specifications & Cost Tracking If you want OpenClaw to accurately track your token usage costs, you can update the cost fields from 0 to the current Azure pricing. Here are the specs and costs for the models we just deployed: Model Specifications Model Context Window Max Output Tokens Image Input Reasoning gpt-5.2-codex 400,000 tokens 16,384 tokens Yes Yes gpt-5.2 272,000 tokens 16,384 tokens Yes No Current Cost (Adjust in JSON) Model Input (per 1M tokens) Output (per 1M tokens) Cached Input (per 1M tokens) gpt-5.2-codex $1.75 $14.00 $0.175 gpt-5.2 $2.00 $8.00 $0.50 Conclusion: And there you have it! You have successfully bridged the gap between the enterprise-grade infrastructure of Microsoft Foundry and the local autonomy of OpenClaw. By following these steps, you are not just running a chatbot; you are running a sophisticated agent capable of reasoning, coding, and executing tasks with the full power of GPT-5.2-codex behind it. The combination of Azure's reliability and OpenClaw's flexibility opens up a world of possibilities. Whether you are building an automated devops assistant, a research agent, or just exploring the bleeding edge of AI, you now have a robust foundation to build upon. Now it is time to let your agent loose on some real tasks. Go forth, experiment with different system prompts, and see what you can build. If you run into any interesting edge cases or come up with a unique configuration, let me know in the comments below. Happy coding!AgentCon New York - Come One Come All for FREE
On March 9, 2026, #AgentCon lands at Nasdaq, Times Square, bringing together developers, engineers, and innovators shaping the future of AI agents. Expect deep‑dive talks, hands‑on learning, practical demos and plenty of networking with the AI community. This isn’t just another AI event, it’s where builders meet to talk real code. ➡️ Register now!
