AI DEVELOPMENT
AI coding agents for teams that are shipping, not experimenting. They review pull requests, write the tests nobody writes, patch dependencies and shepherd releases - inside your repository, your CI and your review rules, with a human merging.
What a software development agent does
An AI software development agent is a scoped worker inside your engineering workflow: it watches a specific event - a pull request opened, a build failed, an advisory published - does one job against it, and reports back through the tools your team already uses.
The distinction that matters is between a coding assistant and a coding agent. An assistant helps a developer who is typing. An agent works when nobody is typing: overnight, on the queue, on the twelve dependency advisories that arrived while your team was asleep. Both are useful. Only one adds capacity.
These agents do not merge. They open, comment, propose and check. A human keeps the merge button, and that is a design decision rather than a limitation we plan to remove.
You probably need this if you recognize these
- Pull requests wait on one senior reviewer, and everything queues behind them.
- Test coverage is agreed to be important and nobody has time.
- Dependency updates pile up until one of them becomes an incident.
- Releases depend on a person remembering the sequence.
- Nobody knows why the nightly job failed, because nobody looked before it passed again.
What you get
A review agent
Reads every pull request against your conventions, flags defects and risk, and says plainly what a human still needs to read.
A test-writing agent
Generates tests for uncovered paths, runs them, and only proposes the ones that pass and genuinely assert something.
A dependency agent
Watches advisories, opens patch pull requests, runs the suite and reports what broke.
A release agent
Runs the sequence, checks the gates, and rolls back on a failed health check.
Your rules, encoded
Conventions, review standards and merge policy expressed as agent configuration rather than a wiki page nobody reads.
Observability
What each agent did, on what, and how often a human overrode it.
How an agent handles a pull request
AI coding agents for software teams
Trigger
A pull request opens. The agent picks up the diff, the linked issue and recent history.
Self-healed - retried with fallback tool. Human not required.
Read
It reviews against your conventions, not generic best practice pulled from the internet.
Check
Tests, coverage, dependencies and obvious security patterns are checked in one pass.
Comment
Findings are posted inline, ranked, with the reasoning visible and the noise suppressed.
Propose
Where a fix is unambiguous, it offers a patch as a suggestion rather than a commit.
Hand over
A human reviews and merges. The agent never holds the merge button.
Agents in the engineering department
Code Review Agent
Reviews every pull request for defects, style and risk, and flags what a person should read.
Test-Writing Agent
Writes tests for the paths coverage keeps missing, and proves they fail without the fix.
Build & Release Agent
Runs the release sequence, checks the gates and rolls back on a failed health check.
Where these agents live
These agents work through the same interfaces your engineers do. If a human can do it with an API token, so can the agent - with a narrower one.
- Code repositories
- CI/CD pipelines
- Issue trackers
- Chat
- Package registries
- Security advisories
- Artifact stores
- Observability tooling
Platform names are shown as examples of the categories agents connect to. They are not partnerships or endorsements.
What these agents may and may not do
Autonomy boundary
They may read, comment, open branches and propose changes. They may not merge, force-push, or alter protected branches.
Approval gates
Every change reaches production through your existing review and release policy. Nothing bypasses it because an agent proposed it.
What stays human
Architecture, security-sensitive review, anything touching secrets, and the merge.
Logging
Every comment, patch and pipeline action is attributed to the agent and traceable to the event that triggered it.
How the work runs
Typical ranges from our engagement model (doc 04 §5), not a quote.
| Stage | Typical | What happens |
|---|---|---|
| Pilot | 3-10 days audit, then 2-4 weeks | One agent on one repository, in comment-only mode, measured against what your reviewers would have said. |
| Build | 3-8 weeks | Additional agents, your conventions encoded, CI wiring, observability and override tracking. |
| Release | 1-2 weeks | Widened repository by repository, with the override rate watched at each step. |
| Managed | ongoing, optional | Rules kept current as conventions change, models re-routed as cheaper ones clear the bar. |
What we measure
- Comment-only
- How every agent starts, before it proposes anything (Target)
- 100%
- Of agent actions attributed and traceable to a trigger (Target)
- Override rate
- Tracked per agent - a rising rate is a defect, not a tuning problem (Target)
- Your baseline
- Review turnaround and coverage, measured before and after (Yours)
“ Until then these are design targets, not results.
What this looks like in practice
Self-healing hive
Reference scenario · SaaS and infrastructure. Nightly incident volume absorbed before the on-call phone rings.
Reference scenario - a composite build illustrating our method. Figures are modeled and the model is shown.
Frequently asked questions
Will these agents merge code without us?
No. They read, comment, open branches and propose patches. The merge button stays with a human, and changes travel through your existing review and release policy. This is a deliberate design decision: the value is in removing the waiting and the drudgery, not in removing the judgment.
Does our code leave our environment?
That depends on the deployment you choose, and it is a decision we make with you rather than for you. We contract for zero training rights on your code with every model provider we use, and we can deploy so that repositories stay inside your own boundary. Where a provider's terms cannot meet that, we route to one that can.
Won't it just generate review noise?
That is the main failure mode, so it is the main thing we tune. Agents start in comment-only mode and are measured against what your reviewers actually flagged, with low-value comments suppressed rather than ranked. A review agent whose comments people scroll past has failed, and the override rate tells us that before your engineers have to.
How does it know our conventions?
We encode them: your style rules, review standards, architectural boundaries and the things your team has agreed never to do. That encoding is a deliverable. It is also usually the first time some of those rules have been written down anywhere.
What about the tests it writes - are they any good?
Only the ones that pass and genuinely assert something are proposed. A generated test that passes against broken code is worse than no test, so the agent verifies that each test fails without the fix before offering it. Your engineers still review them like any other contribution.
Can it work on a legacy codebase?
Yes, and the returns are often better there, because the conventions are undocumented and the review burden falls on whoever remembers. Coverage and dependency work tend to pay off first. The audit tells you which of your repositories is the right place to start.
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