Building AI Agents Your Team Can Actually Maintain

The agent graveyard problem

Most teams building AI agents hit the same wall: the prototype works brilliantly in demos, then starts failing in unpredictable ways in production. The root cause is almost always the same — the agent was designed to succeed on the happy path, not to handle the full distribution of real inputs, tool failures, and edge cases.

What maintainable agents look like

Explicit state management

Every agent should have a clear, inspectable state model. If you can't answer "what is the agent trying to do right now, and what has it tried so far?" from a log, the agent will be impossible to debug when it goes wrong.

Tool contracts with error handling

Every tool your agent calls should have a defined contract: what it takes, what it returns on success, and what it returns on failure. Agents that receive ambiguous tool errors and try to reason their way through them produce the most expensive failure modes.

Structured output, always

Agents that return free text at any step in the pipeline are a maintenance liability. Use structured output (JSON schema, Pydantic models) throughout, so downstream steps can parse deterministically and failures surface immediately.

Evals from day one

Define your success criteria before you build the agent, not after. A task completion eval — even a simple one — gives you a baseline to measure against and catch regressions as you iterate.

Orchestration patterns that scale

• Planner-executor: One model plans the steps, another executes them. Easier to debug and re-run from any step.
• Supervisor-worker: A coordinator routes tasks to specialised sub-agents. Good for complex multi-domain tasks.
• Human-in-the-loop: Built-in review steps for high-stakes decisions. Critical for any agent touching external systems or data.