Agentic Development Platform Build
Last verified: October 2026· engagement
A brat-style multi-agent harness tuned to your stack. Your devs delegate tasks — refactor a module, add tests, fix a class of bugs across the codebase — with safety rails and human-in-the-loop.
- Outcome
- A multi-agent harness running against your repo
- Timeline
- 3–6 months
- Pricing
- $100–300k build, $10–25k/mo ops
- Buyer
- CTO, VP Eng (50+ engineers)
The problem
You want your engineers to delegate codebase-scale tasks to agents — refactors, test coverage, fixing a class of bugs everywhere — but doing it safely against a real repo needs more than a chat box.
What we do
- Build a multi-agent harness tuned to your stack and conventions.
- Add safety rails, scoped permissions, and human-in-the-loop checkpoints.
- Integrate with your CI, review, and deploy pipeline.
- Operate and tune it, or hand it off for your team to run.
What you get
Built on our open source
brat — A multi-agent harness for AI coding tools — crash-safe state, parallel execution, one CLI.
What a multi-agent harness has to solve
The chat window is not the hard part; the hard part is everything around a long-running agent working against a real repository. State has to survive a crash, because a harness that loses an hour of work when a process dies will not be trusted with anything that takes an hour. Work has to be decomposed into tasks that each fit in a fresh context window, with the repository itself as the shared state between them, because long sessions degrade in predictable ways and the fix is structural rather than a bigger window.
Parallelism needs a boundary. Investigation parallelises cleanly — several agents can read, search, and report without interfering. Modification does not: agents writing to different parts of the same codebase at once produce plausible, mutually inconsistent changes, and the merge cost exceeds anything the parallelism bought. So the harness we build runs reads wide and writes narrow, with an explicit queue and an ownership rule for anything that touches files.
Then the execution boundary: what an agent may run, what it may reach on the network, which paths it may write to. This is the part that determines whether the platform is safe to point at a production repository, and it is the part most homegrown harnesses skip because it is invisible until something goes wrong. Human-in-the-loop is a design decision rather than a feature toggle — we decide with you which classes of change land as a pull request for review, which can merge behind green CI, and which an agent may never attempt.
Expect the first month to look slower than doing the work by hand, and plan for that rather than being surprised by it. Getting crash-safe state, the execution boundary and the task queue right is unglamorous infrastructure work with no visible output, and it is what separates a harness people trust from one abandoned after it loses an afternoon's progress. The compounding starts once engineers stop watching each run, and they only stop watching once the thing has been reliable for long enough to earn it.
When this is not the right engagement
This is infrastructure for delegating at organisational scale, which rules out several situations. It is a poor fit when:
- The codebase is not agent-ready yet. A harness on top of a repository agents cannot work in amplifies the problem; do the readiness work first.
- Your team is small enough that one engineer with a good agent setup covers the need. This is infrastructure for delegating at organisational scale.
- You want an off-the-shelf product. This is a build tuned to your stack, and if a commercial tool fits you should buy it instead.
Common questions about this engagement
Do you work under NDA?
Yes — we sign your mutual NDA before any data or repo access. For audits we prefer read-only access to start; for builds we work in a clean repo under your ownership.
Will we own what you ship?
Always. You own the code, the runbooks, the dashboards. We are explicitly set up to hand off and transition out, not to create dependency.
Vendor-agnostic — what does that mean in practice?
We integrate with what you already run — OpenAI, Anthropic, open-weight models on your cloud, your CI/CD, your observability stack. If a hosted vendor solves it, we will not reinvent it; if a self-hosted tool is the right answer, we will not pretend the hosted one is.
How is this different from a Big Four consulting deck?
We are the engineers doing the work, not analysts handing recommendations to a different team. The deliverable is working software in your repo, not a slide deck.
Can you work with our in-house AI team instead of replacing them?
Yes — most of our engagements pair with an internal owner and ramp them up to run the system after we leave. Many of our best engagements start with "we hired an AI team, help us get them productive."
How is this different from just giving everyone a coding agent?
A coding agent is a tool an engineer drives. A harness is infrastructure that runs tasks against your repository with crash-safe state, an execution boundary, a queue, and rules about what merges automatically versus what lands as a pull request. The difference shows up when work runs longer than one session or when several things run at once — which is precisely where an ad-hoc setup loses an hour of progress to a crashed process and nobody trusts it again.
Can agents run in parallel safely?
For reads, yes, and that is where most of the speedup comes from — several agents investigating, searching and reporting do not interfere. For writes, generally no. Agents modifying different parts of the same codebase concurrently produce plausible, mutually inconsistent changes, and the merge cost exceeds anything the parallelism bought. The harness we build runs reads wide and writes narrow, with an explicit ownership rule on anything that touches files.
Guides for this work
What is agentic coding?
Agentic coding is letting an AI coding agent plan, edit across files, run tools and tests, and iterate in a loop toward a goal — powerful for the first 80% of a change, and quietly expensive on the last 20% if you skip standards and review.
Agentic codingContext engineering for coding agents
Context engineering is the work of deciding what a model sees on each turn: what goes in, in what order, and — the part teams skip — what gets left out. On coding agents it is usually the difference between an agent that lands a clean diff and one that thrashes, because almost every agent failure that looks like a reasoning failure is really a context failure.
Agentic codingIs your codebase ready for AI agents?
Agents don't fail randomly on a repo — they fail predictably on the same things that slow down human engineers: fuzzy boundaries, missing tests, undocumented conventions. Readiness is just those weaknesses paid down deliberately.
Let’s scope it on a call
Thirty minutes with an engineer. We’ll tell you straight whether this is the right first move for your team.