01
Developer judgment
Training your developers on when to trust agent output and when to push back — reviewing generated code for the failure modes that look correct, and knowing which tasks to hand over in the first place.
Agentic engineering consultancy
Coding is solved.Your team isn't.
AI writes the code now. It doesn't teach your developers when to trust it, secure the agents running against your repos, or bridge the gap between what the business actually wants and what the agent builds.
That gap — not the code — is what's slowing you down.
Model output is no longer the bottleneck. The bottleneck is a team that can't tell a good agent result from a plausible one, an agent with more access to your infrastructure than any contractor would get, and a requirement that meant one thing to the stakeholder and another to the model. Those are organisational problems, and they don't get fixed by a better prompt.
How I close it
Three pieces of work. Most teams need all three, and they're usually best taken in this order.
02
DevSecOps guardrails
Sandboxing, credential scoping, and CI gates for agent output — so an agent working against your repositories has exactly the access it needs and nothing it doesn't, with its work verified before it merges.
03
Spec-driven development
The two-way translation layer: turning stakeholder intent into specs an agent can execute, and turning agent output back into language your stakeholders can actually trust.
Who you'd be working with
Sergei Beilin
Ph.D. (math), software engineer, systems architect, mentor.
Independent consultant on ML/OR projects and system design, with experience in large enterprises and startups.
Let's find your bottleneck.
A free 30-minute call. Tell me where agentic coding is stalling in your team and I'll tell you which of the three it is — whether or not you work with me afterwards.