Your engineers, plus a fleet of agents that actually ship
Most teams bought the AI coding tools and got a productivity rumour. We install the standards, pipelines, guardrails and measurement that turn coding agents into delivery throughput you can prove.
Buying the tools was the easy part
Almost every engineering team now has AI licences. Very few have shared standards, guardrails, or a number that says whether any of it worked. That gap is the whole problem.
Adoption without a system
Three developers use agents brilliantly, ten use autocomplete, the rest quietly opted out. No shared context, no shared prompts, no shared standards, so the gains stay stuck with individuals instead of compounding across the team.
Agents nobody governs
Most organisations now run agents against real systems, and only a small minority have a governance model for them. Unreviewed MCP servers, over-scoped tokens and unsandboxed execution eventually become somebody's incident.
No idea if it is working
Deployment frequency looks great while review queues, rework and defect rates quietly grow. Without the right measurement you cannot tell genuine acceleration from debt you have not been billed for yet.
The six things that turn agents into throughput
Not a strategy deck. Working configuration in your repositories, your pipelines and your tenant, delivered by people who build software this way every day.
An agent-ready codebase
Agents are only as good as the context they are given. We make your repositories legible to them, so the same request produces the same quality no matter which developer types it.
- AGENTS.md and CLAUDE.md conventions per repository
- Spec-driven workflow: specify, plan, tasks, implement
- Curated context, architecture decisions and house standards
- Test coverage where agents need a safety net
Tool selection and rollout
An in-editor assistant, a terminal agent and a cloud agent solve different problems. We help you choose per job instead of standardising on one vendor and hoping it is still the right one next quarter.
- Evaluated on your own codebase, not on a vendor demo
- Licence mix and real cost per seat modelled up front
- Two-layer rollout: broad assistant plus deep agentic tooling
- Champions, pairing sessions and an internal playbook
Agents in the pipeline
The compounding gains come when agents work while nobody is watching: on every pull request, on the test suite, and on the upgrade backlog that nobody volunteers for.
- AI review on every pull request, tuned to your standards
- Generated and maintained test suites
- Background agents for migrations and dependency upgrades
- Agent-assisted incident triage and postmortems
Governance and security
Before agents touch production systems, somebody has to own what they are allowed to reach. In most companies nobody does. We make it explicit, reviewable and enforceable.
- MCP server registry with review and approval workflow
- Sandboxed execution and least-privilege tokens
- Secret scanning and static analysis on agent-authored code
- Mapped to SOC 2, ISO 42001 and the EU AI Act
Measurement that survives scrutiny
Sooner or later somebody asks what the licences actually bought. We take the baseline before the rollout starts, so the answer is evidence instead of anecdote.
- DORA and DX Core 4 baseline captured before the pilot
- Adoption, acceptance and rework rates per team
- Change failure rate watched as closely as delivery speed
- A quarterly number you can defend to the board
Enablement that sticks
Tools do not change how people work. We train your team in the way we genuinely work, then hand the practice over, because the goal is your independence rather than our retainer.
- Hands-on workshops on your own repositories
- A champion per squad, supported rather than abandoned
- An internal centre of excellence once you are big enough
- Documented in your wiki, not ours
Tool-agnostic, on purpose
This market rewrites itself every quarter. We build on open standards like MCP and AGENTS.md, so the work survives the next tool you switch to instead of being locked to whoever won this year.
One squad proving it in six weeks, not a year-long programme
The large consultancies package this as a four to five month framework exercise before anything ships. We think you should see agents working on your real code long before that.
Assess, 2 weeks
We map how your team actually builds today, audit the AI usage and licences you already have, and capture a measurement baseline. You get a readiness score and a ranked list of what to fix first.
Pilot, 4 to 6 weeks
One squad, one real codebase. Repo standards, agent workflows, AI review and guardrails go in, and we ship actual features with them. Judged against the baseline, not against a feeling.
Industrialise, 6 to 8 weeks
Whatever the pilot proved becomes organisation-wide: MCP registry, governance model, pipeline agents, security scanning and the internal playbook your champions will teach from.
Scale and hand over
Squad by squad, with your champions leading the rollout and us on call rather than in the room. We are explicitly working towards leaving.
Three ways in
Fixed scope and fixed price, with no discovery phase that bills for six weeks before you learn anything.
Readiness review
Two weeks. We audit how your team builds, where AI has already leaked in, and what is genuinely blocking adoption.
- Maturity assessment and readiness score
- Tooling and licence recommendation
- Risk and governance gap list
- Prioritised roadmap with effort estimates
Agentic pilot
Six weeks on one squad and one real codebase, with a measured before and after rather than a demo.
- Repo standards live in your codebase
- AI review and test generation on real pull requests
- Measured against a DORA and DX baseline
- A go or no-go decision backed by data
Rollout partner
Ongoing. We industrialise what the pilot proved and take it across the organisation with your champions in front.
- MCP registry and governance model
- Security and compliance mapping
- Squad-by-squad enablement
- Quarterly measurement review
We are not consulting on something we read about
Siesta Labs builds client software with these tools every day:
- Agents write and review code in our own pipelines, on production systems
- We built and operate our own AI platform, Siesta AI
- Azure-native, so governance fits the tenant you already run
The questions every CTO asks us
Curious what your team would actually gain?
Two weeks, one readiness review, and a ranked list of what to fix. Bring your messiest repository.
Book a readiness review