Summarize conversations into handoff documents for seamless agent-to-agent transitions. Compacts current conversation context into a structured document saved to the OS temp directory, enabling fresh agents to resume work without re-reading full chat history Automatically suggests relevant skills the next agent should invoke based on conversation content Avoids duplication by referencing existing artifacts (PRDs, plans, ADRs, issues, commits, diffs) by path or URL rather than repeating their content Redacts sensitive data including API keys, passwords, and personally identifiable information before handoff Tailors handoff focus based on user-provided arguments describing the next session's goals

End-to-end deployment, evaluation, and management of AI agents on Microsoft Foundry. Covers the complete agent lifecycle: creation from starter samples, containerization and ACR push, hosted or prompt agent deployment, invocation, batch evaluation, and prompt optimization Includes specialized sub-skills for deploy, invoke, observe (evaluation and prompt optimization), trace analysis, troubleshooting, and dataset curation from production traces Supports project and resource provisioning, RBAC management, quota tracking, and model deployment with intelligent routing across regions and SKUs Requires .foundry/agent-metadata.yaml as the source of truth for environment-specific configuration, datasets, and evaluation test cases

Unified access to Azure AI services: Search, Speech, OpenAI, and Document Intelligence. AI Search supports full-text, vector, hybrid, and semantic search with AI enrichment capabilities like entity extraction and OCR Speech service enables speech-to-text transcription (real-time and batch), text-to-speech with neural voices, speaker diarization, and custom models MCP server integration provides direct tool access via azure__search and azure__speech commands; falls back to CLI and SDK when MCP is unavailable Includes OpenAI model access, DALL-E image generation, embeddings, and Document Intelligence for form extraction and OCR

Configure Azure API Management as an AI Gateway for models, MCP tools, and agents with built-in governance policies. Supports semantic caching (60-80% cost savings), token rate limiting, content safety filtering, and jailbreak detection across AI backends Add Azure OpenAI, AI Foundry models, or convert existing APIs to MCP tools as managed backends with load balancing Includes five core policy categories: authentication, semantic cache lookup, token limits, content safety, and token metrics for observability Requires Azure CLI for configuration and testing; integrates with managed identity for secure backend access