Agentic Engineering Adoption
Your team has AI tools.
You don’t have results yet.
A 2025 randomised study found experienced developers were 19% slower with AI coding tools. Not because the tools don’t work, but because the engineering practices around them are missing.
slower with AI tools
(METR study, 2025)
agent-written PRs/week
at Stripe
revenue per employee
(Cursor vs $600K avg)
The gap between these realities is not the model you use. It’s engineering discipline.
We help teams close that gap.
What we do
From honest assessment to compound results
Assessment
Where does your team actually sit on the autonomy spectrum? Which practices are missing? What’s causing the slowdown? We find the real answers, not the comfortable ones.
Roadmap
A phased, 16-week adoption plan tailored to your stack and team. Context first, validation second, tooling third, compounding last. Concrete deliverables at every checkpoint.
Hands-on
We don’t hand you a PDF and leave. We write your first AGENTS.md with you. We configure your quality gates. We’re there when the J-curve hits and your team wants to quit.
Sound familiar?
The three things we hear in every first conversation
Tools without workflow redesign produce the J-curve: things get worse before they get better. Most teams quit during the dip.
If agents produce slop, that’s an engineering problem. Missing context, conventions, validation. Fix the inputs, the outputs fix themselves.
They’re right, and wrong. Their implicit knowledge doesn’t transfer to agents. Make it explicit and the dynamic reverses.
The approach
16 weeks from assessment to compound effect
Phase 0: Context Foundation
AGENTS.md for every repo. Domain knowledge in markdown. Secrets audit. Agent gateway running. Pilot project selected.
Phase 1: Validation
IDE agents deployed. Strictest linting, 100% test pass rate, anti-mocking rules. Pre-commit hooks. First agent-assisted feature shipped.
Phase 2: Tooling
Agent-assisted PR review. Custom CLI tools for your friction points. Hard blocks defined. CI/CD redesigned for AI-generated code at volume.
Phase 3: Compound
Codebase cleanup sprint. Team-wide adoption. All tools in shared repos. Standardised workflows. Compound effect measured and visible.
Results to expect
Feature delivery
within 12 months
Bugs caught
pre-release
PR turnaround
at 6 months
Daily agent
adoption
Safety & sovereignty
Your code stays yours
Open source
(MIT licence)
Any LLM provider
Switch anytime
EU & Swiss
compliance
Self-hosted
options
Zero vendor
lock-in
Who this is for
✓ Good fit
Engineering teams of 3-50. You have AI tools but aren’t seeing results. You care about code quality, not just speed. You’re willing to change workflows, not just add tools.
✗ Not a fit
Looking for a magic tool that fixes everything. Not willing to invest in documentation or process change. Need someone to build your product for you.
Let’s talk
No commitment, no pitch deck. Just a conversation between engineers about where you are and what’s possible.