A useful way to think about any AI agent: agent = model + harness. The model supplies reasoning. The harness is everything else: the code and infrastructure that feeds the model, runs its actions, enforces rules, checks results and records what happened.
Two teams can use the identical model and get dramatically different outcomes. The difference is almost always the harness. As models improve, the gap between a demo and a dependable production system is increasingly closed by engineering the harness well. That discipline is harness engineering.
What is inside a harness?
| Component | What it does |
|---|---|
| Agent loop | Repeats observe, decide, act until the task is done or a limit is hit (see the agent loop). |
| Tools & integrations | Typed, documented functions the model can call: APIs, databases, browsers, code execution. |
| Context management | Assembles, trims and compacts what the model sees each step (see context engineering). |
| Permissions & sandboxing | Least-privilege access, isolated execution, approval gates for risky actions. |
| Guardrails & hooks | Deterministic checks before and after actions: policy rules, validation, spend limits. |
| Verification | Tests, schema checks and critic steps that confirm the work is correct before it ships. |
| Memory & state | Durable storage for plans, progress and learned facts across steps and sessions. |
| Observability & evals | Traces, logs, cost tracking and automated evaluation suites. |
Design principles that hold up in production
Put determinism where you can
Models are probabilistic; business rules are not. Anything that must always be true (never email a customer without approval, never exceed a refund limit) belongs in code in the harness, not in a polite instruction in the prompt.
Make verification a first-class step
Agents are far more reliable when they can check their own work against something objective: run the tests, validate against a schema, compare to a source of truth. Give the loop a way to know whether it succeeded, and feed failures back as information.
Constrain the action space
Fewer, well-described tools, scoped credentials and narrow permissions reduce both mistakes and blast radius. Start tight and widen deliberately.
Keep humans in the loop where stakes are high
Approval checkpoints for irreversible or sensitive steps keep autonomy useful and safe. Over time, evidence from logs tells you which checkpoints can be relaxed.
Instrument everything
You cannot improve what you cannot see. Full traces of each step, tool call, token count and outcome turn every failure into a regression test.
A simple way to evaluate a harness
- Can you replay any run step by step and see exactly why it acted?
- What is the worst thing the agent can do with its current permissions?
- Is there an automated check that fails when quality regresses?
- What happens when a tool errors, times out or returns garbage?
- Can you swap the underlying model without rewriting the system?
If those are hard to answer, the harness, not the model, is where to invest next. This is the work we do in our enterprise AI agent engagements.