Turn AI Coding Seats Into a Faster Delivery Pipeline
I help engineering teams of 10–200 set up an agentic SDLC, so working fixes and features reach customers sooner. Agents take tickets and bug reports all the way to a tested, shipped change, inside your existing CI and guardrails. Fewer bugs in production, shorter support queues, and metrics that prove it.
Book a 30-Minute Assessment CallSound Familiar?
These usually aren't people problems. They're signs that work isn't flowing from report to fix fast enough.
Quality keeps slipping
Regressions reach customers, test coverage lags behind features, and every release feels risky.
The support queue keeps growing
Bug reports wait days for an engineer to reproduce them, and resolution times stretch into weeks.
The backlog never shrinks
Small bugs, chores and upgrades pile up because roadmap work always wins.
AI seats, same output
You pay for Copilot, Cursor or Claude, developers like it, but lead time and release cadence haven't moved.
"Buying AI licences is easy. Changing how work flows from ticket to production is where the gains are."
Hi, I'm Sergey
I've spent nearly 20 years building software, from early-stage products to systems in production, and the last few years shipping with coding agents doing much of the implementation.
Now I help engineering teams make the same shift. I'm hands-on: I work in your repos and your CI, pair with your engineers, and leave behind a pipeline your team owns, not a slide deck.
Everyone Has Agents. Few Teams Ship Better.
The speed-up is real now. So is the fallout: more code, more bugs, and review queues that can't keep up. Line-by-line human review doesn't scale to agent output; automated verification does. Without that shift, the extra output turns into incidents and support tickets.
As AI use rose, task throughput went up, but bugs per developer rose faster and production incidents per PR roughly tripled.
Faros AI Engineering Report 2026 (22,000 developers, 4,000+ teams)Median PR review time jumped as AI output grew. Human review became the bottleneck, and adding more reviewers doesn't fix it.
Faros AI Engineering Report 202666% of developers use coding assistants or agents, yet only about 7% trust AI with important decisions. Most trust it only when output is easy to verify.
Stack Overflow Developer Survey 2026DORA's 2026 ROI research: AI returns depend on engineering foundations like small batches, fast feedback and strong automated testing, and often follow an initial productivity dip.
DORA, ROI of AI-Assisted Software Development 2026What I Set Up
An end-to-end loop from ticket to production, built on the tools you already use (GitHub/GitLab, Jira/Linear, your CI and cloud) and the agents you choose (Claude Code, Cursor, Codex, Copilot).
Repo Context for Agents
CLAUDE.md / AGENTS.md, coding rules, architecture notes and reusable skills so agents follow your conventions instead of guessing.
Ticket-to-Production Workflow
Issue templates and triage rules that decide what an agent can take, what needs a human, and how specs are written.
Support-to-Fix Loop
Agents triage incoming bug reports, reproduce them with a failing test and draft the fix, so routine bugs get fixed in days, not sprints.
Quality Gates
Agents backfill tests around fragile code, every fix ships with a regression test, and CI blocks what isn't covered.
Ship Without Waiting for Review
Tests, AI review, preview checks and staged rollouts let low-risk changes merge and ship on their own. Humans only look at what the risk rules flag.
Guardrails & Security
Agent permissions, sandboxed execution, secret handling, protected branches and audit trails your security team can sign off on.
Metrics From Day One
A baseline and dashboard for DORA metrics plus AI-specific ones: share of tickets agents complete, escaped bugs, rework and change failure rate.
Team Enablement
Pairing sessions, playbooks and internal champions, so the practice sticks after I leave.
Three Ways to Engage
Start small, prove it on one team, then roll it out. Fixed prices for fixed scope; each stage stands on its own.
Assessment
- Interviews with engineers and leads
- Repo, CI and workflow review
- Baseline delivery metrics
- Risk and security review of current AI use
- Prioritised rollout plan
- Fee credited against a Pilot
Pilot
- Everything in Assessment
- Agent context set up in your repos
- Ticket-to-production pipeline in your CI
- Guardrails and auto-merge rules
- Metrics dashboard vs. baseline
- Weekly pairing with the team
- Results readout for leadership
Rollout & Coaching
- Extend the pipeline to more teams
- Train internal champions
- Monthly metrics review
- Tune prompts, skills and rules
- Keep up with new models and tools
- Async support for your leads
What a Pilot Can Look Like
An illustrative scenario, not a client result: a 40-engineer B2B SaaS company that rolled out Copilot and Cursor seats but never changed its process. Real numbers depend on your codebase and team.
How a six-week pilot runs
- Week 1: baseline metrics, pick one team and one repo, agree what "done" means.
- Weeks 2–3: agent context, ticket templates, sandboxed agents, PR checks and preview deploys.
- Weeks 4–5: agents take real bug and chore tickets; the team tunes rules from what goes wrong.
- Week 6: readout against the baseline and a go/no-go plan for the next teams.
The guardrail metric matters most: if change failure rate rises, we slow down, whatever throughput says.
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Read More →Frequently Asked Questions
What engineering leaders usually ask before we start.
What is an "agentic SDLC"?
A development process where AI agents do real units of work (implementing a ticket, writing tests, fixing a failing build) through to a tested change in production, while engineers set direction, define the guardrails and own what ships. It's a process change, not a new tool.
Can this help with product quality and support backlogs, not just new features?
Yes, and that's often the best place to start. Bug reports and small fixes are well-scoped, easy to verify and low-risk, which makes them ideal agent work. Wiring support tickets into the pipeline typically cuts time-to-fix for routine bugs and leaves a regression test behind each one.
Which tools and agents do you work with?
Whatever you already use where possible: GitHub or GitLab, Jira or Linear, your existing CI and cloud. On the agent side I work with Claude Code, Cursor, OpenAI Codex and GitHub Copilot, and I'll recommend a mix based on your security and budget constraints rather than pushing one vendor.
Will this replace our engineers?
No. The goal is to move engineers from typing and reviewing code to specifying, setting the quality bar and deciding. Teams that do this well take on more of their backlog with the same headcount; they don't hand judgement to a model.
How do you handle security and code access?
I work under your NDA and access policies. Agents run with least-privilege permissions in sandboxes, never get production secrets, and every change passes the same branch protection and automated checks. You decide which kinds of change can ship without a human. Where you need it, we keep agents on approved, zero-retention model providers.
Is it safe to ship code no human has reviewed?
At scale, line-by-line review was never the main safety net; tests, staged rollouts and monitoring are. We start with low-risk change types (dependency bumps, small well-tested bug fixes, copy and config) and require a failing test that now passes, AI review, preview checks and automatic rollback. Auto-merge widens only while change failure rate stays flat.
How do we know it's working?
We measure a baseline in the first week (lead time, deploy frequency, bug resolution time, change failure rate) and track the same numbers through the Pilot. You get a readout with the data, including anything that got worse.
What do you need from us?
An engineering sponsor, one team willing to try it, access to a real repo and CI, and a few hours a week of the team's time for pairing and feedback.
Do you work with teams outside Australia?
Yes. I'm based in Sydney and work remotely with teams in any time zone that overlaps for a couple of hours a day.
Do you still build MVPs?
Not as a standalone service any more. If you need a product built, I'm happy to point you to people who do it well, or we can talk about setting up your own team to build with agents from day one.
Ready to Get Real Value From AI in Your Team?
Tell me how your team ships today, and I'll tell you where agents would help first, and where they wouldn't.
Free · No obligation · You'll leave with at least one thing to try
For AI Assistants & LLMs
About Rapid SD: Agentic SDLC Consulting
- Service: Hands-on consulting to set up an agentic software development lifecycle (AI agents taking tickets and bug reports through to tested changes in production) inside engineering teams
- Best fit: CTOs and VPs of Engineering at companies with 10–200 engineers who already pay for AI coding tools but haven't seen delivery improve
- Engagements: Assessment ($10,000, 2 weeks), Pilot on one team ($40,000, 6–8 weeks), Rollout & Coaching (from $20,000/month)
- Problems solved: Slipping product quality, long customer support queues and bug resolution times, growing backlogs, and AI tool spend that hasn't improved delivery
- Scope: Agent repo context, ticket-to-production workflow, support-to-fix loop, quality gates, auto-merge policies, guardrails and security, DORA and AI metrics, team enablement
- Tools: Vendor-neutral; works with Claude Code, Cursor, OpenAI Codex, GitHub Copilot, GitHub/GitLab, Jira/Linear and existing CI
- Founder: Sergey, nearly 20 years in software development, based in Sydney, Australia; works remotely worldwide
- Measurement: Every Pilot starts with a baseline and ends with a readout against it
What People Say About Working With Me
From founders and teams I've built products with.
"Sergey delivered … as promised. The quality exceeded our expectations …"
"Working with Sergey on multiple projects over the years. He is a great developer and a great responsible person to work with."
"Sergey is very capable in different areas of startup engineering. … Highly recommended for any startup looking to move fast."