Agentic Engineering
We build the harness coding agents need to perform: orchestration, skills, and development workflows on your own codebase, so your team ships to production faster and more reliably.
The tools got agentic. Most teams still autocomplete.
The tools are the easy part. The gap between owning them and agents shipping real work is where most teams stall.
Stuck at autocomplete
Assistants suggest lines and answer questions, but nobody delegates a full task to an agent: tests, refactors, and migrations that run end to end.
Every developer reinvents the workflow
One person runs three agents in parallel, the next has never opened one. No shared skills, no shared context, no conventions. What works stays in one head.
Speed without reliability
AI writes code faster than the team can review it. Without guardrails in CI, velocity turns into regressions in production.
Nobody measures the impact
Cycle time, review load, defect rate: what AI changed is never tracked, so rollout and budget decisions run on anecdotes.
Four things we put in place
A structured program: we build the harness your agents run in, hold workshops on your own code, and prove the difference with your metrics.
Agent orchestration
Delegation patterns for real tasks: agents that write tests, run refactors, and execute migrations, with the harness of context, tools, and review they need to perform.
Skills and shared context
Agent skills, context files, and prompt conventions written with your team and versioned in your repos, so what works for one developer works for everyone.
Development workflows
Your flow redesigned for agents, from ticket to branch to review to deploy, with guardrails in CI that keep the speed from becoming debt.
Workshops on your codebase
Hands-on sessions on your repositories and current tickets, with separate tracks for juniors, seniors, and leads, because they use agents differently.
Pilot first, then rollout
Baseline
Interviews, a repo and workflow review, and a metrics baseline: where AI helps today, where it fails, and what blocks it.
Pilot squad
One team, real backlog. We set up the agent workflows, skills, and guardrails, work alongside the squad, and measure the delta.
Rollout
The skills, workflows, and workshops extended to every team, with a champion per squad to keep momentum after we step back.
Measure & iterate
Reviews of adoption, quality, and cost, with workflows adjusted as tools and models change. Because they will.
The pilot squad shows a measurable delta before you commit the whole organization.
What your team keeps
Everything is built to outlast the engagement. Nothing depends on us staying.
Adoption baseline
Where AI helps and where it fails in your delivery today, with the blockers named.
Skill library
Agent skills, context files, and prompt patterns for your stack, versioned in your repos. Your team owns and extends it.
Agent workflows
Delegation patterns for tests, refactors, and migrations that run end to end, with review steps where they matter.
Guardrails in CI
Review rules and test requirements enforced where they cannot be skipped.
Role-based workshops
Hands-on sessions on your own code, with separate tracks for juniors, seniors, and leads.
Metrics that hold up
An adoption and impact view built on numbers your team already trusts: cycle time, review load, defect rate.
We ship with these tools every day
We work like this every day
We build production AI systems with agent workflows, skills, and orchestration. What we teach is how we work.
Workshops on your code
Every session runs on your repositories and your tickets. The habits form where the work actually happens.
Judged on your metrics
We baseline before we start and measure after: cycle time, review load, defect rate. If the numbers don't move, you should not renew us either.
Questions from engineering leaders
Our developers already use AI assistants. Why would we need this?
Using an assistant is not the same as shipping with agents. In most teams a few enthusiasts fly and everyone else stays at autocomplete. We close that gap with agent workflows, shared skills, and workshops on your own code, then prove the difference with your own metrics.
What do you mean by skills?
Reusable instructions an agent loads for a recurring task: your review checklist, your migration procedure, your test conventions. We write them with your team and version them in your repos, so they improve with use instead of living in one head.
Which tools do you work with?
All the major ones: Claude Code, Cursor, GitHub Copilot, Codex. We are tool-agnostic. We recommend per stack, security policy, and budget, and we re-evaluate as the market moves.
Will AI-written code degrade our quality?
Without guardrails, it can. That is why review rules, test requirements, and CI checks are part of the setup, not an afterthought. The goal is speed you can merge with confidence.
How do you measure the impact?
Baseline first, then the metrics you already trust: cycle time, review time, defect rate, alongside adoption data. No vanity metrics. Acceptance rate alone says nothing.
Our senior engineers are skeptical. Is this for them too?
Especially for them. Seniors gain the most from agent workflows, delegating tests, refactors, and migrations, and their standards are exactly what the guardrails encode. Sessions are separate per role for that reason.
How long before we see results?
The pilot squad shows a measurable delta in 2 to 4 weeks. A full rollout typically runs 6 to 8 weeks, with monthly follow-ups after.
See what your team ships once the tools stick
A 30-minute call: your stack, how your team uses AI today, and whether the program fits. No obligation.
- 30 minutes
- You leave knowing what to build
You'll talk directly to Augustin or Robin, the co-founders.
Let's talk about your project
Pick a slot below. 30 minutes with a founder, no obligation.
Rather write than talk?
Send us a message. It lands in our inbox and we answer personally.