Services
An agentic software delivery lifecycle.
AI writes code fast. Most teams get faster output and slower delivery. We help engineering teams redesign the lifecycle around agents, so the speed shows up in what ships.
Problem
What we’re solving
- Agents produce more code than the team can review.
- Builds go green without testing what matters.
- Every engineer uses AI differently, so nothing compounds.
What we do
How we help
- A spec-first workflow: intent, acceptance criteria, small units of work
- Verification that agents cannot grade for themselves
- Guardrails on cost, scope and permissions for agent work
- Training so the team can direct, review and own agent output
The package
Agentic Delivery Pilot
One real repository, one real feature, shipped the new way.
- A map of how work moves from idea to production today, and where it stalls
- An agentic workflow set up on one of your repositories
- One real feature shipped through it, with your team
- A playbook and training so the team keeps running it
You get: A working agentic delivery lifecycle on your codebase, a shipped feature to prove it, and a team that knows how to run it without us.
3–4 weeks · one repository · free 20-min intro call first
Fixed price, scoped on the call
How it works
Three phases
1
Map how work really flows today and where AI already sits in it.
2
Set up the workflow and ship one real feature through it together.
3
Hand over the playbook and stay close while the team runs it alone.
Who it’s for
Built for
For CTOs and engineering leaders whose teams already use AI coding tools and are not yet seeing it in delivery.
FAQ
Questions buyers usually ask
Which AI coding tools do you work with?
The ones your team already uses, or the ones that fit your stack. The lifecycle matters more than the tool: the same practices work across the major coding agents.
Is this about replacing engineers?
No. Agents change what engineers spend their time on: less typing, more specifying, reviewing and owning outcomes. Teams that skip that shift get more code and slower delivery.
How do you keep quality up when agents write the code?
By never letting the thing that wrote the code be the only thing that checks it. Clear acceptance criteria up front, independent verification, and a human who owns the decision to ship.
Does our code leave our environment?
The pilot runs in your repositories and accounts, under your policies. We agree which tools and data are in scope before we start.
What is agentic AI?
AI that takes actions rather than only answering questions: it can plan steps, use tools and systems, and carry a task through. In software delivery, agents write code, run tests and propose changes for people to review.
How is agentic AI different from traditional automation?
Traditional automation follows fixed rules. Agents make judgment calls within limits you set, which is why they can handle messier, multi-step work, and why they need guardrails and review.
What human oversight do agents need?
It depends on what the agent can change. Anything irreversible, such as payments, customer-facing actions or production deploys, needs a person to approve it. Lower-risk work can be reviewed after the fact.
Should we build agentic AI in-house or bring in a partner?
For most mid-market teams: start with a partner, learn the patterns on one real project, then bring it in-house. The pilot is designed so your team can run it without us.
What should we ask an AI development partner before hiring them?
Ask to see something they have shipped and still runs, how they handle failures, who owns the code, and how they verify AI-written work. A partner who cannot explain their process plainly is a warning sign.
Related reading
Before you book
Ready to talk
If you need help deciding whether training, strategy, or implementation is the right starting point, we can map it together.
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