Building an agent harness is less about clever prompts and more about disciplined engineering. The model is the engine. The harness is the car. This guide walks through the build in the order you should actually do it.
If you are new to the concept, start with our companion piece, What Is an Agent Harness?, then come back here.
Step 1: Define the Task Boundary
Before writing code, write down exactly what the agent is allowed to do and what "done" means. A harness without a stop condition will burn tokens forever. Capture the goal, the allowed tools, the success criteria, and the hard limits.
Step 2: Build the Core Loop
The loop is the smallest unit of an agent. It sends the current message history to the model, reads the response, executes any tool calls, appends the results, and repeats until a stop condition fires.
Set three limits from day one: a maximum number of iterations, a token budget per run, and a wall-clock timeout.
Step 3: Build the Tool Registry
Each tool needs four things: a clear name, a description the model can reason about, a typed input schema, and a handler function. The description is the interface the model actually reads, so treat it as prompt engineering.
- Name: short, verb-first, unambiguous.
- Schema: validate arguments before the handler runs.
- Handler: return structured results, never raw exceptions.
- Errors: return a readable message the model can recover from.
Step 4: Add Context and Memory
Long tasks overflow the context window. A practical harness keeps three layers: the system instructions, a rolling summary of the session, and retrieval from a vector store for domain knowledge. Summarize old turns instead of dropping them silently.
Our guide to vector databases covers the retrieval layer in depth.
Step 5: Sandbox and Guardrails
Any tool that touches a real system needs limits. Apply permission scopes, timeouts, and rate limits. Pause for human approval before irreversible actions such as sending money, deleting data, or publishing content.
Step 6: Instrument Everything
Emit a structured trace for every step: the prompt, the model output, every tool call with its arguments and result, latency, and token counts. This is the difference between debugging in minutes and guessing for days.
Step 7: Evaluate Before You Ship
Build a small suite of realistic tasks with known-good outcomes. Run it on every change. Track success rate, average steps, and cost per run. Regressions in agent behaviour are invisible without an eval harness.
Treat your eval suite as the agent's unit tests. If a change lowers task success rate, it does not ship.Step 8: Deploy With Fallbacks
Production is unreliable by nature. Add model fallbacks so a provider outage does not stop your agent, queue long-running runs, and store run state so a restart can resume rather than lose work.
Common Mistakes
- No iteration cap: the agent loops and the bill climbs.
- Vague tool descriptions: the model calls the wrong tool or invents arguments.
- Silent tool errors: failures get swallowed and the agent confidently reports success.
- No trace: production failures become impossible to reproduce.
- Unscoped credentials: a prompt injection becomes a real incident.
The Build Order, Summarised
- Task boundary and stop condition
- Core loop with hard limits
- Tool registry with typed schemas
- Context and memory strategy
- Sandbox and approval gates
- Structured tracing
- Eval suite
- Deployment with fallbacks
Laurel Jar designs and builds agent harnesses for teams moving from demo to production. If you want a second pair of eyes on your architecture, get in touch.