01
Developer judgment
Equipping developers with methods to critically review agent output — spotting plausible-looking failure modes and knowing when to delegate vs. when to take the wheel.
Agentic engineering consultancy
Coding is solved.Your team isn't.
AI writes code in seconds. But building real confidence in production requires developer judgment, secure guardrails around your repositories, and clear alignment between business intent and model execution.
That gap — not code generation — is where friction happens.
Raw model speed is rarely the bottleneck. Real productivity depends on the engineering practices around it: giving developers practical methods to evaluate AI output critically, sandboxing agent access so systems stay secure, and establishing a shared language between stakeholders and models. These are collaborative workflow challenges that prompt tweaks alone cannot solve.
How I help
Three practical building blocks to make agentic engineering secure, effective, and sustainable.
02
DevSecOps guardrails
Sandboxing, least-privilege credential scoping, and automated CI gates — ensuring agents operate safely within defined boundaries and code is verified before merge.
03
Spec-driven development
A structured two-way translation layer: turning business intent into unambiguous specs models can follow, and communicating agent output back in language stakeholders 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 talk through your setup.
A 30-minute introductory call. We'll look at how your team is adopting coding agents, discuss potential friction points, and outline pragmatic next steps — whether we end up working together or not.