Answer
How should you structure a project for a coding agent?
So a change can be checked without anyone reading it: one command to build, one to test, tests that defend boundaries.
So that correctness is checkable by running something. One command to build, one to test, and tests that actually fail when a boundary is crossed. Everything else follows from whether a change can be evaluated without reading it.
The property that decides whether agent work is worth having is not how clean the code is; it is whether a proposed change can be evaluated cheaply. An agent can generate a change in a minute, and if evaluating it takes an hour of an experienced person's attention, the arrangement produces a queue rather than throughput. Everything worth doing to prepare a codebase is a way of moving evaluation from reading to running.
That means the tests are the interface, not the documentation. A suite that passes when the code is wrong is worse than no suite, because it produces confident approval. A suite that fails when a real invariant is broken is the mechanism that lets a change be accepted without anyone tracing the reasoning. Time spent making tests meaningful is the highest-return preparation available, and it pays for human work at the same rate.
Second, a build and a test that run in one command with no local knowledge. An agent cannot be told which three services to start and which environment variable is set on the senior developer's machine, and neither can a new colleague. The friction is identical for both, and the reason to fix it for agents is that they hit it every session rather than once.
Third, boundaries that exist in the code rather than in convention. If it matters that one module does not reach into another, a lint rule, a type boundary or a dependency check states it in a way that fails. A convention held in the team's heads is invisible, and an agent will cross it reasonably and produce a change that is locally sensible and structurally wrong.
Fourth, keep the working tree honest. A repository with a hundred uncommitted changes, generated files under version control and a dozen dead branches makes it impossible to see what a change actually did, which is the single most important thing during review. The diff is the artefact being evaluated, and anything that adds noise to it is a direct tax on every review.
Finally, write down the things that are true and not deducible. Which of two similar modules is the live one. Why an obvious approach was rejected. Which parts are load-bearing for something not visible in the code. These are the same things a new hire needs, and the same argument applies: it is worth writing once because it is asked repeatedly.
A codebase where the only way to know a change is safe is for someone experienced to read it is a codebase where an agent multiplies review rather than work.
Siddharth Sharma, Context Theory
Related questions
Does this mean a legacy codebase cannot be worked on by agents?
It means the work should start where the checking is possible. A legacy area with no tests is a poor first target; a well-tested module in the same repository is a good one. The productive sequence is often to have the agent add characterisation tests around a legacy area first, which is useful independently and makes subsequent changes evaluable.
Is a monorepo better or worse for this?
Neutral in principle and usually worse in practice, because the reachable surface is larger and boundaries are more often conventional than enforced. What matters is whether the agent's working scope can be narrowed to the part it should touch, which a monorepo makes a deliberate act rather than a default.
METHOD
Every figure below carries its source and the date it was verified. Nothing on this page is asserted.
The numbers on this page.
| What | Value | Specific to |
|---|---|---|
| Sub-15-minute compliance — automated routing vs manual only | 62.5% vs 39.1% | Category-wide |
| Close rate — response under 5 minutes vs over 24 hours | 32% vs 12% | Category-wide |
2026 speed-to-lead benchmark · verified
Optifai speed-to-lead benchmark · n=939 companies · Q2 2025–Q1 2026 · verified
What is specific to this page.
| Kind | Claim | Check it against |
|---|---|---|
| Workflow | The constraint on agent-assisted development is evaluation cost rather than generation cost, so a codebase where change correctness is only establishable by an experienced reader converts agent output into a review queue rather than into throughput. | Timing the review of a representative change against the time to generate it. |
| Software | A test suite that passes while an invariant is broken produces confident approval and is worse than an absent suite, so the value of testing infrastructure to agent work is measured by whether it fails on real violations rather than by coverage. | Deliberately breaking a known invariant and checking whether the suite fails. |
| Response | Conventions held by the team but not expressed as a lint rule, type boundary or dependency check are invisible to an agent, which will cross them reasonably and produce changes that are locally sensible and structurally wrong. | Listing the architectural rules the team enforces in review and checking which are expressed in code. |
| Procurement | The diff is the artefact under evaluation, so uncommitted changes, generated files under version control and stale branches impose a direct cost on every review by obscuring what a change actually did. | Producing a diff for a representative change and counting the lines that are not part of the intended change. |
Each row would be wrong on another industry's page. Where a sourced figure exists it is in the table above instead; these are the constraints that shape the work and do not happen to be numbers.
Start with the measurement.
Reading about a benchmark is not the same as knowing your own number. The audit produces yours, measured rather than estimated.
$497 · delivered in 5 business days · credited against month one