103 posts · Page 1 of 9. ← Blog
The patterns that worked, the traps we fell into, and what we'd do differently.
What worked, what was hard, and what we'd do differently. Real numbers: 18 agents, 90 days, 5 governance policies, 4 provider swaps.
Running agents on Cloud Run, exposing via A2A, and wiring into production systems.
Cloud Run deployments, agent-to-agent communication, load balancing, and production observability.
How to instrument agents for observability, error handling, and audit logging.
Callbacks to middleware: composable decorators for audit, retry, token enforcement, and OpenTelemetry integration.
How to port ADK's model hard-codes to MAF's provider factory pattern.
Zero-code provider swaps: Ollama (dev), OpenAI (staging), Azure Foundry (prod). Same agents, different models.
How to port tools, add policy enforcement, and integrate OPA.
From ADK functions to MAF governed tools. Adding policy enforcement, DLP, approval gates, and OPA integration.
How conversation threads replace session state; how to track token usage across agent chains.
Sessions to threads: porting multi-turn state from ADK to MAF. Token budgeting, long-term memory, and conversation audit trails.
How to port ADK's orchestration callbacks to MAF builders without losing control.
How to port ADK's orchestration callbacks to MAF builders without losing control. The executor pattern: you own the loop.
The philosophy, trade-offs, and what we learned converting 18+ agents in 3 months.
The philosophy, trade-offs, and what we learned converting 18+ agents in 3 months. Provider abstraction as the foundation for portable agents.
How to instrument multi-agent systems with OpenTelemetry, propagate trace context across an in-memory bus, and build a layered evaluation pipeline — from real-time policy gates to async LLM-as-judge to SLO-based trust scoring. Everything I learned building Genie.
Google's GKE AI infrastructure docs list ~40 integrations. Here's a field map of which ones actually matter when the workload is a HIPAA-aware multi-agent medical AI, and where the gaps sit.
How a 200-line Go handler turns an audit log and an eval store into a regulator-friendly answer to
Request → N-eyes approve → window-of-time → automatic expiry, with every transition written to a hash-chained audit log. The package that closes Gap #1 from the PCSE map.