Architecting Production AI Agents That Don't Break

Most agent demos work because the demo is the test. Production is different: inputs are adversarial, tools fail, and a single bad action has consequences. Building agents that survive that environment is an architecture problem, not a prompting problem.

Separate planning from execution

The single most important decision is to split the planner from the executor. A planner decomposes a goal into discrete, inspectable steps; an executor carries them out one at a time. This separation makes plans reviewable before any action touches a real system, and it gives you a natural place to insert approval gates.

Give tools typed contracts

An agent's tools are its hands. If those hands are loosely defined, behaviour becomes unpredictable. Every tool should have a typed schema for inputs and outputs, validation at the boundary, explicit timeouts, and a retry policy. When a tool call fails, the agent should observe a structured error, not a stack trace it then hallucinates around.

• Validate every tool input against a schema before execution
• Return structured, machine-readable errors the agent can reason about
• Bound every tool with timeouts and idempotency keys
• Keep a registry so tools can be audited and permissioned

Ground decisions in memory

Agents need two kinds of memory: episodic (what happened in this run) and semantic (durable knowledge). Without grounded memory, agents repeat work, contradict themselves, and lose the thread across long tasks. Back episodic memory with fast storage and semantic memory with a vector index over verified context.

An agent without verifiable memory is just an expensive way to make the same mistake repeatedly.

Make safety a layer, not a prompt

Guardrails written into a system prompt are suggestions. Real guardrails are code: allow-lists for actions, rate limits, confidence thresholds, and human approval gates for anything consequential. The agent proposes; the policy layer disposes.

Instrument everything

You cannot improve what you cannot replay. Capture full trajectories — every plan, tool call, and observation — so failures can be reproduced and scored. An evaluation harness that replays real trajectories turns 'the agent feels worse today' into a regression you can actually fix.

Get these five things right — planning, typed tools, memory, a policy layer, and observability — and you have an agent operations will trust. Skip them, and you have a demo.