A Guide to Harness Engineering for AI Agent Workflows

Agent-first software workflows can begin with an empty git repository and a team of coding agents. By shipment, application logic, tests, continuous integration (CI) configuration, documentation, and internal tooling may all come from agents. The shift moves engineering work to a different layer. The job now is to design the specs, constraints, tools, and feedback loops that agents use to produce reliable work.

This guide covers what harness engineering is, how spec-driven development and the Model Context Protocol fit in, why agents need a product context layer, and how to build agent workflows that hold up under audit.

What Is Harness Engineering?

Harness engineering follows a single principle. Humans provide instructions, while agents execute them. Our job shifts from writing code to designing environments, specifying intent, and building feedback loops that let coding agents perform reliable work. We prioritize work, translate user feedback into acceptance criteria, and validate outcomes. When an agent struggles, that signal identifies missing documentation or guardrails, and we feed it back into the repository.

In coding-agent systems, “harness” refers to the core agent loop and execution logic underlying the agent experience. The same idea can be framed more broadly as the collection of specifications, quality checks, and workflow guidance that govern the loops an agent runs. In shorthand, the agent is the model plus its execution loop. The model writes code. The execution logic checks whether that code is correct and aligned with intent. Building and maintaining that execution layer means working on the loop itself, rather than generating code prompt by prompt.

Why the Engineer’s Job Changes When Agents Write the Code

Progress slows when the environment is underspecified, even when the agent can generate code. The agent lacks the tools, abstractions, and internal structure to make progress toward high-level goals. The missing work is scaffolding, the tools and structure that help agents do useful work.

Engineers build the structure that agents read. We also build feedback loops as infrastructure, with tools that let agents verify their work in context after producing a first answer. In one common agent-loop pattern, an engineer writes docstrings and assertions, the agent generates the implementation, and failed assertions produce tracebacks that the environment can feed back into another attempt.

Agent legibility shapes day-to-day results. Agents can only use information available in their active context while running. Details stored in Google Docs, chat threads, or individual memory do not guide the system unless they are surfaced to it. Versioned materials such as code, markdown, schemas, and executable plans are what the agent can work from.

Context layers are organized so that an agent can reason about the business domain directly from the layer. In the same way we improve code navigability for new engineering hires, the goal is to enable an agent to understand boundaries and intended behavior directly from durable project artifacts. Agents are most effective in environments with strict boundaries and predictable structure.

How Spec-Driven Development and MCP Set the Stage for Harness Engineering

Agents build more reliably against durable specs. Spec-driven development (SDD) puts the specification at the center of the workflow. Engineers describe what to build, refine it through structured phases, and let the agent implement it. The reframe matters because development moves from code as the source of truth to intent as the source of truth.

The Model Context Protocol (MCP) connects large language model (LLM) applications to external data sources and tools through a client-host-server architecture. Servers expose three primitives. Resources are data sources like file contents or database records, tools are executable functions an agent can call, and prompts are reusable interaction templates. MCP closes a practical gap in agent work, because engineering work rarely stops at editing files. It also involves checking tickets, querying production data, testing in a browser, and writing changelog entries. Much of that work happens outside the repo. MCP is one way that external context reaches the agent.

Together, SDD and MCP give agents stable inputs to read against, whether that context arrives as a spec file or through a structured server.

Why Agents Need a Product Context Layer

When knowledge remains in chat threads, scattered docs, and people’s heads, agent work exposes the gaps. Common context failure modes include context poisoning, where incorrect information compounds. Context distraction has the agent repeat past behavior instead of reasoning afresh. Context confusion lets irrelevant tools cause wrong choices. Context clash leaves the agent stuck on contradictory information.

Repository knowledge alone does not close this gap because a codebase does not contain specific categories of context. Without semantic context, agents can generate code that violates architectural principles. Without historical context, they can reintroduce previously resolved problems. The repo holds code history but not the business authorization context, the human approval chain, or the task-level justification behind a decision.

The examples are the kind every engineering team recognizes, like a field named calc_temp_v2 that two engineers know is a deprecated staging column, or a metric redefined mid-year. None of that survives cleanly in the repository, and agents cannot reliably recover it after the fact.

For regulated, multi-team products, the gap turns into an exposure. Regulated environments need decision provenance, and partial answers and limited traceability make it harder to show how an answer was produced. Those links become part of compliance evidence.

How the Traceability Information Model Gives Agents Structure and Context

Artifact types, relationship types, and enforcement rules can be defined before requirements work begins through a Traceability Information Model for requirements (TIM). It specifies which traces are required (a system requirement must link to at least one test case), which are optional, and which are prohibited. A spreadsheet-based matrix records links at a single point in time and diverges the moment anything changes, whereas a TIM is a governing schema that the tooling enforces. Missing downstream items get flagged automatically rather than surfacing at a milestone review after orphan requirements have already accumulated silently.

That model gives the spec an enforced relationship layer. SDD writes a structured, behavior-oriented artifact that expresses functionality and guides coding agents. A TIM defines the relationships that those artifacts must hold to each other. For spec-driven systems, that means making specs machine-readable, enforcing spec checks in CI, and constraining agents to files linked to specific spec IDs. A TIM provides the enforced relationship layer underneath that practice, which is why regulated domains that mandate requirements-to-implementation traceability can treat it as part of the workflow.

Free-form documentation drifts, and nobody notices until it matters. By making intent explicit, specifications reduce the ambiguity that pushes artificial intelligence (AI) systems to infer missing requirements, an arXiv analysis of SDD notes, and regulated domains often mandate traceability that SDD provides naturally. For companies mapping European Union (EU) AI Act traceability obligations into engineering workflows, bidirectional traceability across artifacts can help connect requirements, design, code, and tests. The structure the agent must respect is the structure the auditor can later read.

Governance and Auditability in Harness Engineering

High agent throughput raises governance stakes. When agents contribute changes at tremendous speed, questions about code quality, maintainability, and accountability scale with the volume of changes. When we bring agents into regulated product development, the discipline of governing AI within systems engineering is still taking shape across the software development lifecycle.

Audit trails need more than commit history. In regulated software companies, audit evidence often requires end-to-end traceability for audits that link each deployed change to its initial request, the code’s authorship, review and approval by the appropriate people and the test and build artifacts. AI-generated test cases still need to be version-controlled and traceable. Changes still require formal change control, and audit logs must capture when AI was used and by whom. Missing that infrastructure carries a direct cost because the events have to be reconstructed after the fact. Inside Jama Connect, any work performed with AI is versioned and documented as AI-generated, creating audit evidence that external AI tools cannot replicate. Teams running AI outside a governed system of record carry compliance risk when those artifacts are later required for audit.

When humans did not write the code, auditors and engineering leaders need decision lineage that survives beyond any single commit or memory. Every line of code should trace back to a justified requirement, and every requirement should trace forward through design and into verified test cases. Logged action records should show which agent identity performed the action, which resource was changed, the time of the action, the justification, and the outcome. Change control over AI artifacts should document the circumstances of AI use in compliance records so accountability endures beyond the people involved.

When we adopt agent-driven development, we should build governance documentation, agent inventories, and audit log infrastructure early. Agent governance and audit evidence should use the same records.

How Jama Connect Supports Harness Engineering

Jama Connect® is the Product Context Layer for engineering organizations. It connects requirements, risks, tests, SysML models, code repositories, simulations, defects, reviews, approvals, verification evidence and a requirements management and traceability platform, including TIMs that enforce a product context model for harness engineering. A TIM defines which artifact relationships must exist, and Live Traceability™ flags every downstream artifact for reassessment when an upstream requirement changes, so gaps surface during development rather than at a milestone review.

That same structure feeds directly into the AI layer. Jama Connect Advisor™ scores requirements against International Council on Systems Engineering (INCOSE) and Easy Approach to Requirements Syntax (EARS) standards at the point of authoring. It catches ambiguity before a coding agent inherits it. Requirements records can govern agents and satisfy an audit when they are derived from live data rather than assembled after the fact.

Build an Agent Workflow You Can Audit

Agent-driven development exposes the next bottleneck as usage scales, governance. If your team is scaling AI agent usage faster than your governance structures can keep up, the same governed structure can carry both agent work and audit evidence. Start a free 30-day trial of Jama Connect.

Frequently Asked Questions About Harness Engineering

What is the difference between harness engineering and spec-driven development?

Spec-driven development defines what an agent should build by making the specification the source of truth. Harness engineering defines how the agent’s environment is structured to execute reliably against that spec, including guides, feedback sensors, scaffolding, and constraints. SDD can be treated as a specialized discipline within agent workflow design.

Does harness engineering mean engineers stop writing code?

Engineers still write code. In agent-first projects, humans may contribute less application code directly while writing docstrings, assertions, custom linters, structural tests, and rules files that constrain the agents. The work shifts toward engineering the conditions for correctness.

How does a TIM help AI coding agents?

A TIM defines the artifact types and relationships that must exist before requirements are written, giving agents a structured, enforced model of intent to build against. When an upstream item changes, downstream artifacts are automatically flagged, keeping generated output coherent and surfacing coverage gaps during development rather than at an audit.

Why does agent-driven development need an audit trail?

When agents write code at high volume, no human can explain every change from memory, and regulated standards still require traceability from request to approval to verified test. An audit trail captures which agent did what, under what justification, and against which requirement. Without it, incident response and compliance reviews become longer and harder to reconstruct.

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Mario Maldari

Director of Product and Solution Marketing at Jama Software

Mario Maldari is Director of Product and Solution Marketing at Jama Software, where he focuses on requirements management and systems engineering solutions for regulated industries. He brings over 2 decades of experience across solution architecture, technical pre-sales, and software quality, and holds patents in traceability and structured data. Before moving into product marketing, he worked directly with engineering teams to solve compliance and requirements challenges.

What Is Harness Engineering? A Software Engineering Guide