Key Takeaways
- Agent harness engineering is the discipline of designing everything around an AI model that turns it into a working agent: prompts, tools, runtime, hooks, credentials, and observability.
- Agent harness engineering was developed by and for developers. When the agent’s user is not a developer, the user cannot maintain the harness, and the platform team has to enforce it instead.
- Boomi’s MCP gateway enforces agent harness engineering at the protocol layer: tool curation, vault-backed credentials, per-user attribution, and definition integrity across every MCP server in scope.
What is Agent Harness Engineering?
A large language model is a text generator. By itself, it cannot run code, call an API, or take any action in the world. To turn a model into an agent that takes real actions, you have to surround it with the prompts, tools, runtime, and rules that let it operate. That collection is the harness.
Agent harness engineering is the practice of designing the harness deliberately, the way you would engineer any production system. The term emerged in late 2025 and early 2026 as teams shipping agents noticed that the same model produced very different behavior depending on what was wrapped around it.
This matters because the model is no longer the bottleneck for most production AI deployments. Across the agent products on the market today (Claude Code, Cursor, Codex, Aider, Cline), the underlying models are often interchangeable. What defines each product is the harness. The clearest evidence: Vivek Trivedy and his team at LangChain moved a coding agent from outside the Top 30 to Top 5 on Terminal Bench 2.0 by changing only the harness, lifting pass rate from 52.8% to 66.5% with the same underlying model. The work is documented in Improving Deep Agents with Harness Engineering. Same model, same task, dramatically different results.
What Goes Into Building an Agent Harness?
A harness has six main components. Every production agent has all six, whether they were designed deliberately or not. The difference between a working agent and a brittle one is usually how much of the harness was designed on purpose.
- The system prompt and skill documents. Files like AGENTS.md and CLAUDE.md that give the model persistent instructions, project conventions, and policies.
- Tools. Local functions, MCP servers, command-line tools, and API integrations that the agent can invoke, each with a description the agent reads at runtime.
- The runtime. Where the agent executes: filesystem access, sandbox boundaries, shell availability, and network egress.
- Orchestration logic. How the agent structures its work: subagents, model routing, planner/generator/evaluator splits.
- Hooks and middleware. Deterministic code that runs before, during, or after agent actions to enforce rules, because the model is not reliable enough to enforce itself.
- Observability. Logs, traces, token accounting, and error attribution. Without it, you cannot iterate on the harness.
The Principles of Agent Harness Engineering
The agent harness engineering discipline has converged on two principles that show up across every serious piece of public writing on the topic.
Every observed mistake becomes a permanent constraint.
When an agent makes a mistake, you do not just tell it to do better next time. Harnesses reinforce boundaries to prevent repeated errors: a new rule in AGENTS.md, a hook that blocks the destructive command, or a rewritten tool description.
Good harnesses trace each new constraint to a specific thing that went wrong.
Every harness component encodes a specific assumption about what the model cannot do on its own.
Hooks exists because models cannot reliably refuse a destructive action, tool descriptions exist because the model cannot infer the tool’s preconditions, and skill docs exist because the model does not know your codebase conventions.
When the model improves and the assumption is no longer needed, the component should be removed from the code. This framing comes from Anthropic’s engineering team in Effective Harnesses for Long-Running Agents.
Together, these two principles produce harnesses that grow in capability over time without growing in noise.
How to Do Agent Harness Engineering: A Practical Workflow
Building a harness for the first time is not complicated, but like many agentic infrastructure tools, it requires discipline and strategy at scale. Here is a common workflow that shows up across teams doing this well in production.
1. Describe intended tasks with high precision
Define exactly what the agent should do. Be specific. “Reconcile invoices” is not a task definition. “Given a list of open invoices and a list of payments received in the last 30 days, match payments to invoices and produce a report of unmatched items” is a task definition. The harness will only be as good as the precision of the task.
2. Build the smallest harness that works
Start small with one model and two or three tools. The smaller your pilot, the easier it will be to identify and add missing constraints. With this, you can continue to add to your harness to build a strong agent management foundation.
3. Diagnose harness failure by identifying what assumption was wrong.
Each failure is the model violating an assumption its developer made about its capabilities. The agent called the wrong tool because the tool description was ambiguous. It hallucinated an API field because the schema was not in context. It ran a destructive command because nothing in the harness flagged it as dangerous.
When the same kind of failure shows up across multiple users or sessions, this is a sign to make a structural change to the harness.
4. Encode the constraint at the right layer
Choosing the right layer is what differentiates a harness from a prompt. Constraints need to be applied directly to the component being adjusted: tools need to be fixed in their descriptions, behavior is encoded to AGENTS.md, and universal barriers are added to a hook.
5. Keep components current
When a model upgrade resolves an assumption you had been encoding, remove the workaround. Components left in place can make the user experience bloated and slow.
Agent Harness Engineering for Non-Developer Users
Everything above assumes that the user is a developer who can read the harness, audit the tool descriptions, and handle the fix when something breaks.
OpenAI’s Codex team shipped a million lines of code using these principles with three to seven engineers. The challenge is that developers are not the user driving enterprise AI adoption right now.
Over the past several months, teams have rolled out MCP-enabled agents past engineering into finance, marketing, operations, and customer success, a shift covered in How to Enable AI for Every Department, Not Just Engineering.
For non-developer users, the harness needs to be in the background of their experience: the harness is defined within the enterprise’s agent management platform and applied to users. Boomi MCP Gateway handles this through profiles and groups.
A Profile is a sub-catalog of your organizational MCP servers and tools, scoped to the needs of a specific role. Marketing can pull from GitHub through read-only tools while engineering keeps write access. Attach a Group from your IdP to a Profile once, and every current and future member is provisioned access.
Originally, agent harness engineering assumed a developer was at a terminal watching an agent fail, and encoding the fix into a hook or a tool description. That works fine until agents become day-to-day tools for business users. You can’t train every department to be a harness engineer, but you can move the harness into infrastructure your platform team already controls.
That’s what Boomi’s AI Gateway does at the protocol layer. MCP Gateway aggregates every MCP server behind one governed endpoint, enforcing scoped permissions per user or agent and forwarding credentials securely instead of leaving them in a config file nobody’s watching. LLM Gateway applies that same identity-aware enforcement to every model call, no matter which harness or client sent it. Agent Control Plane takes it further, governing the full lifecycle of every agent in your environment: discovering what’s running, tracing lineage and token spend, gating high-risk actions behind human approval, and logging it all across native and third-party agents, whatever model sits underneath. The core principle of harness engineering, every observed risk becomes a permanent constraint, stops depending on a developer noticing the failure first.
Ready to see what that looks like from a CISO’s seat? The CISO’s Guide to AI Governance and Control breaks down what to enforce as policy and what belongs in the platform.