Context Engineering vs Harness Engineering: Where Each Fits

Context engineering and harness engineering are complementary disciplines in AI agent development. Context engineering decides what the model sees before it acts: the instructions, facts, retrieved documents, and project state in its input. Harness engineering decides how the model acts: the tools it can call, the loop that runs its steps, the permissions that constrain it, and the recovery logic when something goes wrong.

Confusing the two leads to misdiagnosed failures. A model that gives a wrong answer may have received bad context; a model that takes a wrong action may be working in a harness that does not enforce the right boundaries. Tools like fenbs serve both layers: they supply the project decisions and progress that context engineering needs to load, and they carry the rules the harness is supposed to enforce.

A summary of the three disciplines

What context engineering covers

Context engineering is the practice of choosing and assembling the text that goes into a model call before it executes. The core question is: does this model call have the instructions, facts, and current state it needs to do the right thing? The answer depends on what you include, how you structure it, and what you leave out.

Good context engineering is selective. A model given everything in a large codebase may struggle to find the constraint that matters for the current task. A model given a focused summary of the relevant decisions, the current task, and the rules that apply is more likely to behave predictably. The context engineering for AI agents guide covers the principles in detail.

Context engineering does not include what happens after the model responds. Tool calls, action execution, and session management belong to the harness. The distinction is useful when debugging: if the model gives the wrong answer but the answer is consistent with what it was told, the problem is in context; if the model says the right thing but the wrong action happens, the problem is in the harness.

Common context engineering problems include stale facts in a system prompt, retrieved documents that contradict each other, rules buried so far into the context that the model ignores them, and project state that was accurate at session start but has since changed. The context engineering vs prompt engineering guide explains where one ends and the other begins.

What harness engineering covers

Harness engineering builds the execution environment around the model. It defines which tools the model may call, what each tool does when called, and what constraints prevent the model from taking actions outside the intended scope. It also handles the parts of a session the model does not control: authentication, rate limits, error responses from external APIs, and safe shutdown.

The harness is where security boundaries are enforced. A model that has been given a context saying "do not write to production" cannot enforce that rule on its own; the harness must reject the action before it executes. This is the same principle that makes permission enforcement in application code important: you cannot trust user input to self-limit, and you cannot trust a language model to self-limit either.

Harness failures tend to be visible and immediate: a tool call throws an exception, a permission check rejects an action, or a process exits unexpectedly. Context failures tend to be subtle: the model acts confidently but on outdated or incomplete information. This asymmetry means harness problems are often caught in testing, while context problems may only surface in production when an action seems correct but is based on wrong premises.

The harness is also where observability lives: logging what the model was given, recording the tool calls it made, and capturing timing data. Without that record, debugging a failure that occurred three steps into a multi-tool run is difficult. A model that took an unexpected action is far easier to diagnose when the harness recorded exactly what it was told and what it decided to do at each step.

What loop engineering covers

Loop engineering is the specific practice of designing the control flow inside a multi-step agent run. It covers how steps are sequenced, what conditions trigger a retry, when a step's output is fed back as input for the next step, and what causes the loop to exit cleanly.

A simple agent that answers one question has no loop to engineer. An agent that searches a codebase, proposes a change, verifies the change compiles, and reports the result is running a loop. Good loop engineering makes that loop predictable: each step either succeeds and passes state forward, fails and triggers a defined recovery path, or reaches a condition that ends the run cleanly.

Loop engineering failures are often the hardest to debug because they involve the interaction between steps. A step that produces an ambiguous output, a retry policy that triggers when it should not, or a missing exit condition that lets the loop run past its intended end can all cause problems that do not appear in any single step's logs. Writing explicit guards and exit conditions is more reliable than relying on the model to decide when to stop.

How the three disciplines interact

The three disciplines are not separate phases; they overlap in practice. Context engineering determines what information the loop carries between steps. Harness engineering determines which tools each step may call. Loop engineering determines how many steps run and in what order. A failure at any layer can look like a failure at another.

For example, an agent that loops indefinitely on a failing step could be a loop engineering problem (no exit condition on repeated failures), a harness problem (the tool call never returns an error the loop can act on), or a context problem (the model was told to keep trying until it succeeds, with no limit stated).

Working through the layers in order helps: first check whether the context was correct, then whether the harness enforced what it should, then whether the loop logic handled the outcome correctly. The context engineering for Claude Code guide shows this in a specific tool. The context engineering in Cursor guide covers another.

Where shared project state fits

One practical question all three disciplines share is where the project's current state lives between runs. Context engineering needs to load it. The harness needs to update it after a run. The loop may need to check it between steps. If the state lives in a session variable, it disappears when the session ends. If it lives in a file, it requires the harness to read and write it correctly.

A connected external tool keeps that state available across sessions, tools, and team members. The context engineer writes the query that loads the relevant state. The harness engineer writes the tool call that saves updated state after a run. The loop engineer decides which step triggers each. Keeping the state outside the agent is what makes it available to the next session, whether that session uses the same model or a different one.

How to do this with fenbs

1. Create a free account and add the agent project to fenbs.
2. Write the rules your agent must follow as Decisions and rules in fenbs. These belong in both the context (so the model knows the rules) and the harness (so the execution layer enforces them).
3. Connect your agent to fenbs via MCP from Integrations. At the start of each session or loop run, the agent reads the current project state: rules, current tasks, and Where we left off.
4. At the end of each run, the agent saves what it completed and what remains. This updated state becomes the context for the next run, whether the same model or a different one handles it.
5. When a run produces a new decision, add it to Decisions and rules before the next run. The context engineering step loads it automatically; the harness enforces it from the same source.