Writing
Production AI Patterns — production-grade AI agent architecture with deterministic control, not demos.
- Most AI demos work. Most AI systems fail in production.
Why production AI in regulated industries needs determinism, observability, and governance beyond what demos require.
- Agents must not execute tools from raw model output
Tool invocation must be validated and decoupled from raw LLM output before anything executes.
- Validation is not a single gate
Production validators are layered decision engines—not a single boolean gate at the end.
- Production agents are distributed systems, not smarter prompts
Multi-step agents need coordination, state, and failure handling like distributed systems—not smarter prompts.
- Agents fail at observation, not only execution
Production agent failures often stem from poor observability across orchestrated steps, not just bad execution.
- Resilience is replay-safe execution, not more retries
Agent reliability requires idempotent, replay-safe execution—not blind retries on multi-step workflows.
- Agents are distributed transaction coordinators
Multi-step agents need compensating actions and rollback when retries cannot restore consistent state.
- The illusion of autonomous agents
Most enterprise agents lack true autonomy—goal ownership, constraint negotiation, and persistence are rarely satisfied.
- Planner risk: is the plan safe to execute?
Production risk concentrates in the planner—validate whether a plan is safe to execute, not just final outputs.
- Agents need authority boundaries
Agents need explicit authority boundaries and permission scopes before planned actions run.
- 10 architecture lessons from building production-grade AI agents
Ten architecture lessons from building planner–orchestrator–validator agents for production environments.
- Production AI agents need a knowledge layer (not just prompts)
Production agents need a structured knowledge layer—not prompt-only context—for grounded reasoning.
- If you don't have evals, you don't have a production agent
Without evaluation loops in CI, you have a prototype—not a production agent you can safely operate.
- The God Agent is dead. Long live multi-agent routing
Enterprise scale needs specialized agents and routing—not one omniscient God Agent.
- Multi-agent systems need a Human Gateway
High-impact multi-agent decisions need an explicit Human Gateway for approval before execution.
- Stop asking your LLM to behave
Security must be a decoupled AI Firewall—not a system prompt asking the model to behave.
- Stop letting your LLM see your secrets
Agentic workflows need bidirectional PII masking through a continuous protection gateway, not one-time front-door filtering.