Harness Engineering
Harness engineering. Agents your team can verify.
Turn a coding agent that needs constant rescue into a workflow your engineers can review. We build the repository context, execution controls and feedback loops around your existing agent.
DIRECT ANSWER
Harness engineering designs the working environment around an AI coding agent: repository instructions, context retrieval, tools, permissions, execution state and verification loops. Wavect implements these controls in your codebase so your team can inspect changes, resume interrupted work and decide when a result is ready for review.
Best for
- Engineering teams whose coding agents repeatedly miss repository rules
- Teams delegating multi-step changes that need tests and human review
Consider another approach
- A new customer-facing AI product with no existing implementation
- A standalone training workshop or an evaluation-only engagement
When should you commission harness engineering?
Your agent edits the wrong layer, loses its place between sessions or declares success while tests fail. We trace one representative task before changing the setup. The deliverable is an implemented agent workflow in an existing repository. For a new AI product, use AI Agents & Products; for wider internal adoption, use AI Enablement.
Give the agent a map it can follow.
We organize concise AGENTS.md instructions, architecture references, build commands and task-specific context retrieval. Repository knowledge stays versioned beside the code. OpenAI’s harness engineering account describes this shift toward environment design and feedback loops. Context engineering supplies useful information; the harness also controls execution and verifies the result.
Bound each action. Preserve the next step.
We define permitted tools, isolated execution where needed, credential boundaries, run budgets and escalation points. Checkpoints record what changed, what failed and what remains. Anthropic’s long-running agent research highlights progress records and incremental work across sessions. New tool interfaces can be scoped under MCP Server Development; an independent threat review belongs to AI Security.
A passing reply is not a passing change.
Illustrative acceptance task: an agent fixes a regression in a test repository. It must reproduce the failure, implement the change, pass the agreed checks and produce a reviewable diff. Interrupt the run, resume from its checkpoint and deny a prohibited tool action. Acceptance requires the expected repository state and recorded test evidence; a success message alone does not pass. This is a proposed test, not a client result.
Check outcomes before increasing autonomy.
We wire linting, tests, architecture checks and review gates into the loop, then record failures, retries, elapsed time and usage against a baseline. Anthropic’s agent evaluation guidance distinguishes the final environment state from what the agent says happened. Dedicated datasets, model comparisons and ongoing application-quality monitoring remain the scope of AI Evaluation & LLMOps.
Leave a workflow another engineer can operate.
You receive versioned instructions and configuration, tool policies, a representative task set, run records and a recovery runbook. Your engineers rehearse a normal run and an interrupted one. Scope, fees and any ongoing support are agreed for your repository; there is no universal autonomy or speed guarantee. Use our software handover checklist to review ownership.
Understand the architecture. Assess the evidence.
Our AI agent harness explainer covers the architecture and build-or-buy decision. The Twinsoft AI case study documents adjacent AI delivery experience; it does not establish harness-engineering outcomes. The first engagement must demonstrate results on your own agreed tasks.
Questions before you start
An implemented workflow around your coding agent: repository context, tool and permission rules, execution checkpoints, verification gates and operating documentation. The agreed task set defines what must work at handover.
Prompt engineering changes instructions sent to a model. Harness engineering also changes the environment in which those instructions run: tool access, state, recovery, checks and review. Better prompts can be one part of the work.
Yes. We assess the repository, agent interfaces and repeatable failure first. We extend the current setup where it supports the required controls. A custom orchestration layer is justified only when the existing tools cannot meet the agreed need.
Harness engineering implements the execution environment and feedback loop around coding work. AI Evaluation & LLMOps owns dedicated evaluation datasets, comparisons and ongoing monitoring of existing LLM applications. A harness uses checks without replacing that broader evaluation scope.
Scope depends on repository complexity, tool permissions, execution infrastructure and the tasks to verify. We agree a bounded assessment and implementation proposal before work starts. Model usage, hosting and ongoing support are identified separately where applicable.
Start with a repository overview, build instructions and a redacted failure example. Implementation needs agreed code and test-environment access. Production credentials are not a prerequisite for the initial assessment; permitted tools and approval boundaries are defined explicitly.
Scope your agent harness
Bring one repository, one recurring agent failure and the task you want to delegate. We agree access, acceptance checks and a bounded implementation scope before work starts.