How Do AI Engineers Build Reliable Agent Systems?

Why do AI agent prototypes fail in production?

Most AI agent prototypes fail in production because they rely entirely on the model's non-deterministic behavior for critical operations. In a demo, the agent might navigate a browser correctly 7 out of 10 times. In production, that 30% failure rate means angry users, corrupted data, and leaked credentials.

The root cause is architectural: engineers treat the model as the system instead of treating it as one component within a system. The Tejas Agent Harness Framework addresses this by defining the harness — everything around the model — as the source of reliability.

How do you architect a harness for a production agent?

Start with the seven-step workflow:

1. Define the task and document failure modes. Run the agent unharnessed and record every way it fails — lying about success, hitting auth walls, looping, context overflow. Do not change the prompt.

2. Build the agent loop with a tool registry. Use an existing SDK (OpenAI, Anthropic) for tool calling. Each tool gets a name, description, parameters, and execute function.

3. Add guardrails. Implement `max_iterations` to kill runaway loops and `max_messages` with a context compressor to prevent context overflow. The naive compressor (keep system prompt + user prompt + last two messages, discard middle) ships fast.

4. Extract into `run_harness_attempt` and `run_harness`. The attempt function encapsulates one run. The harness function wraps it in a retry loop with `max_attempts`. Your entry point shrinks to ~20 lines.

5. Write a deterministic verify step. Inspect the trace — the full history of tool calls and events — and return pass or fail. Never ask the model if it succeeded.

6. Add deterministic handlers. For each obstacle category (login walls, rate limits, form submissions), write a handler that fires in the loop and resolves the issue in code. Secrets come from env vars, never from the model context.

7. Iterate on the harness. Run, read the trace, add handlers or verify cases. The prompt stays constant.

What mistakes do AI engineers make when building harnesses?

The most common mistake is confusing the agent loop with the harness. The agent loop is just the inner while-true cycle. The harness includes the loop, the retry wrapper, all guardrails, the verify step, handlers, and context management.

Second: letting the model self-report success. Non-deterministic models lie. Your verify step must inspect the trace deterministically.

Third: putting secrets in the prompt. Credentials belong in environment variables, injected by deterministic handlers. The model should never see API keys or passwords.

Fourth: building the harness for one specific model. The harness should be model-agnostic — that's the entire value proposition. A well-harnessed GPT-3.5 outperforms an unharnessed GPT-4.

How do you know the harness is working?

Judge success exclusively through the verify step's deterministic output, never the model's self-report. Track metrics across runs: verify pass rate, average iterations to completion, handler activation frequency, and retry rate. A mature harness shows high pass rates with low iteration counts and rare retries.

The trace is your primary diagnostic tool. Every tool call, message, and event is logged. When the verify step fails, the trace tells you exactly where the agent diverged.

Next step: Identify your highest-value agent task, run it unharnessed, document every failure mode, and build your first harness using the seven-step workflow. Start with the minimum viable harness — guardrails, a basic verify step, and one handler — then iterate.