7 February 2025 · 8 min read
Building AI Agents Your Team Can Actually Maintain
Multi-agent systems are powerful - but most prototype agents collapse in production because they weren't designed for observability, failure recovery, or handoff to non-AI engineers. Here's how to build agents that last.
By The AI Internship Team, TAI Labs
From the archive. Originally published 2025-02-07. Tool details, examples and offers reflect that publication date. See our current guides for newer material.
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.
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