Problem
Azure SRE Agent memory (learned patterns, investigation findings, operational context) is agent-local — it never transfers between agent instances. When promoting agent configurations from development to test and production tiers, only repo-based artifacts (skills, knowledge files, custom agents, tools, triggers) are portable. The valuable patterns the agent learned from hundreds of investigations stay locked in the source agent.
Current Behavior
- What promotes via CI/CD: Knowledge files, skill files, custom agent definitions, tool definitions, trigger configs, MCP connector configs, scripts — all sourced from a git repo
- What does NOT promote: Conversation threads, agent memory (learned patterns from investigations), session insights — all are agent-local and geography-local
- Workaround: Manually export valuable findings as
.md files → commit to a shared repo → CI/CD deploys to all agents. This is labor-intensive and loses the semantic richness of the agent's learned context.
Proposed Solution
- Memory export/import API — programmatically export an agent's learned memory as a portable artifact (e.g., structured JSON or markdown bundle) and import it into another agent instance
- Selective memory sync — allow administrators to sync specific memory categories (e.g., "service architecture", "incident patterns", "team preferences") between designated agents
- Memory promotion in CI/CD — integrate memory export into CI/CD pipelines so that validated patterns from development agents automatically promote to production agents alongside skills and knowledge
Use Case
Multi-Tier Deployment
Organizations operating tiered agent deployments (dev → test → prod):
- Dev agents accumulate deep operational knowledge through daily investigations
- When prod agents are provisioned, they start from zero learned context — they don't benefit from months of dev investigation history
- Engineers must re-teach the same patterns, team preferences, and service-specific context to each new agent
Multi-Agent Topology
With multiple product-specific agents, investigation patterns discovered by one agent (e.g., "database connection pool exhaustion correlates with a scheduled job") are valuable to other agents but remain siloed.
Impact
- Medium — accelerates new agent onboarding from days to minutes
- Enables knowledge compounding across the entire agent fleet
- Critical for production readiness: prod agents should inherit dev agents' operational maturity
Environment
Problem
Azure SRE Agent memory (learned patterns, investigation findings, operational context) is agent-local — it never transfers between agent instances. When promoting agent configurations from development to test and production tiers, only repo-based artifacts (skills, knowledge files, custom agents, tools, triggers) are portable. The valuable patterns the agent learned from hundreds of investigations stay locked in the source agent.
Current Behavior
.mdfiles → commit to a shared repo → CI/CD deploys to all agents. This is labor-intensive and loses the semantic richness of the agent's learned context.Proposed Solution
Use Case
Multi-Tier Deployment
Organizations operating tiered agent deployments (dev → test → prod):
Multi-Agent Topology
With multiple product-specific agents, investigation patterns discovered by one agent (e.g., "database connection pool exhaustion correlates with a scheduled job") are valuable to other agents but remain siloed.
Impact
Environment