Integration with TypeSafe AI System One models, including Jev. - TypeSafeChatClient - Adds function invocation, middleware, and telemetry to the TypeSafe transport. - RawTypeSafeChatClient - Provider transport that can emit constrained function calls but does not execute them; it has no function-invocation, middleware, or telemetry layers. - TypeSafeChatOptions - Uses response_format for the required TypeSafe Questions mapping. - Calls always return structured SystemOneResponse data. This provider does not generate free-form chat text. - Every call requires response_format to contain at…
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This file documents the structure and conventions of the Python samples so that agents (AI or human) can maintain them without rediscovering decisions. Durable Task and Azure Functions samples are maintained in the Durable Agent Framework extension. 1. Progressive complexity: Sections 01→05 build from "hello world" to production. Within 01-get-started, files are numbered 01–07 and each step adds exactly one concept. 3. Workflows preserved: 03-workflows/ keeps the upstream folder names and file names intact. Do not rename or restructure workflow…
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Presidio is a Python SDK for detecting (presidio-analyzer) and anonymizing (presidio-anonymizer) PII in text and images, plus CLI, structured-data, and image-redaction components. It is a widely used library: users depend on current detection behavior, and configuration files written years ago must keep working. Correctness and backward compatibility outrank cleverness. The review-side versions of these rules — which the Copilot PR review agent also enforces — live in .github/copilot-instructions.md and .github/instructions/*.instructions.md. Follow them at authoring time; the pre-PR check below verifies…
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Follow .github/copilot-instructions.md for repository-specific development guidance. Before filing a GitHub issue in this repository: Before opening a pull request:
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The end-user quick start is in README.md. This file records how the sample is wired and why, for anyone maintaining it. The sample used to hand-wire around the product, because TableView's type information was withheld from the public winmd and its theme resources did not resolve in a consuming app. It linked raw build output, regenerated its own CsWinRT projection, injected a XAML metadata provider, seeded an internal template part into XamlTypeInfo, compiled the control's theme resources out of the…
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This is Mastering GitHub Copilot for Paired Programming, an open-source educational repository maintained by Microsoft. It is a multi-module, 10-hour course that teaches developers how to use GitHub Copilot as an AI coding partner — from beginner to advanced topics including Agent Mode. The repository is documentation-centric: most content is Markdown-based lesson guides. A small number of code samples exist within specific lessons. Each lesson module follows a consistent structure: Most exercises reference external Codespaces template repos (e.g., github/dotnet-codespaces) rather…
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An agent is an AI system that uses a language model and tools to complete a goal on your behalf. A language model generates responses based on the information in…
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The agent-governance-claude-code/ directory contains the first-party Claude Code plugin package for Agent Governance Toolkit. It wires AGT policy evaluation into Claude-native hooks, markdown commands, and a bundled MCP server.
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Scope: this folder publishes @microsoft/agent-governance-opencode, the AGT governance plugin for OpenCode. OpenCode loads plugins in-process as async JS/TS functions. The plugin entry point is src/index.mjs, which: - caches a single loadPolicy() state per OpenCode process - maps AGT decisions (allow / review / deny) onto OpenCode's contract: - deny → throw new Error(...) from the hook - review → annotate output.args.__agt_review_reason - allow → return undefined - exposes agt_policy_status and agt_policy_check_text as both in-process custom tools and stdio MCP server…
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AgentMesh is a trust-first communication layer for AI agents — providing cryptographic identity, multi-dimensional trust scoring, scope chains, and governance enforcement for multi-agent systems. Architecture: 4-layer trust stack - All data models use Pydantic BaseModel - Identity: AgentIdentity with .sign(data: bytes) -> str (base64 Ed25519 signatures) - Trust scores: 5 dimensions — competence, integrity, availability, predictability, transparency - DID format: did:mesh:{hex} — see the proof-of-possession note under Boundaries: registry/app.py derives DIDs from the public key, while AgentDID.generate mints a random…
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This directory contains cross-language AgentMesh SDKs: These SDKs should stay conceptually aligned with the trust, identity, governance, and MCP security surfaces provided elsewhere in the repo. This directory reflects the repo layout at this point in time. Some language implementations may eventually move into standalone top-level directories such as agent-governance-golang/, agent-governance-rust/, or agent-governance-python/. If that happens, treat the standalone top-level directory as the primary home for new work. This directory should then be treated as legacy/shared-layout guidance for the code…
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Agent-OS is a governance-first kernel for AI agents — a Python framework providing policy enforcement, semantic intent classification, identity management, and execution control for autonomous AI agents. Architecture: 4-layer modular kernel Framework adapters route intervention points through HostSession and receive PolicyEvaluation. Raise the canonical PolicyViolationError.from_evaluation_result(result) for denied or escalated results. Hosts should surface str(error) and use error.evaluation_result.reason_code for structured handling. Use conventional commits: feat:, fix:, docs:, test:, refactor:, chore:
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agent-governance-python/ is the top-level home for first-party published Python packages in Agent Governance Toolkit. Use this directory for reusable SDK/package surfaces that are meant to be consumed directly by external Python users. Applications, dashboards, demos, and larger runtime/product assemblies should stay in the repo root, examples/, or other existing homes unless maintainers explicitly decide to promote them into this package workspace. - Prefer this directory for first-party Python packages that are published independently. - Keep package names, import paths, and…
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Agent-SRE is an SRE toolkit for AI agent reliability — providing SLO management, error budgets, chaos engineering, progressive delivery, cost guardrails, incident management, and observability for autonomous AI agents. Key engines: Use conventional commits: feat:, fix:, docs:, test:, refactor:, chore:
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Agent Governance Toolkit is a multi-package OSS monorepo for runtime governance of AI agents: policy enforcement, zero-trust identity, execution sandboxing, SRE, compliance, examples, docs, demos, SDKs, and publishing pipelines. Use this file for repository-wide routing. When you enter a subdirectory that has its own AGENTS.md, that narrower file takes precedence. This routing reflects the repo as it exists today while reserving room for approved language SDK migrations. Language SDKs may live in standalone top-level directories at the repository root. For…
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The docs/ tree powers the published documentation site for Agent Governance Toolkit: reference material, tutorials, architecture docs, threat modeling, compliance content, package pages, and integration guides. - Be precise and honest; do not claim a feature is shipped unless the repo actually contains it. - Prefer updating an existing page over creating a near-duplicate page. - Use repo-relative links that work from the current document location. - Match existing tone: technical, direct, and evidence-based. - Keep package names, CLI commands,…
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MicrosoftAGENTS.md