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Independent consultant

Agentic Engineering System Sprint

For teams already using Cursor, Codex, or Claude Code: turn AI coding activity into predictable engineering throughput.

The issue is rarely tool access. It is workflow design: how tasks become code, how AI output is reviewed, and how teams stay coherent while shipping fast.

Signals your AI coding setup needs structure

  • Engineers are producing more diffs, but merge confidence is down.
  • PR review time is rising because AI output quality is inconsistent.
  • Each engineer uses Cursor, Codex, or Claude differently, with no shared workflow.
  • Architecture and code style are drifting across parallel streams.
  • Founders still arbitrate routine engineering tradeoffs.
  • AI usage improved local speed, not team-level predictability.

Engagement

How I work with your team

A fixed-scope sprint over two to four weeks. Audit first, redesign second, pilot third, handoff last.

We define human and agent boundaries by task type, create standards for AI-assisted code and review, and install practical repo guardrails that improve merge confidence.

You leave with a working operating loop your team can run without constant founder arbitration.

Talk on LinkedInSee Engineering Reset

What you leave with

  1. Step 1: Map

    Current-state workflow map

    A clear view of your idea-to-merge flow, bottlenecks, and failure points in AI-assisted delivery.

  2. Step 2: Runbook

    Agentic engineering runbook

    Human and agent task boundaries, prompting templates, review standards, and Definition of Done for AI-assisted work.

  3. Step 3: Scorecard

    KPI baseline and 30-day plan

    Cycle time, rework, PR churn, and defect metrics with a concrete adoption cadence for the next month.

What it looks like

  1. Week 1: Diagnostic

    Audit tool usage patterns, review flow, and delivery metrics. Identify where AI output creates noise instead of speed.

  2. Week 2: Workflow design

    Define team standards for prompting, task shaping, PR quality, and ownership. Build the working playbook.

  3. Weeks 3-4: Pilot and handoff

    Apply the model on live tasks, calibrate guardrails, and hand over a 30-day adoption plan with clear owner responsibilities.

This works best when

  • Your engineers already use AI coding tools weekly
  • You care about reliability, not just raw code output
  • You want a bounded implementation sprint, not ongoing advisory

Questions

Is this AI training for engineers?
Partly, but not only that. The core work is operating design: task intake, prompting standards, review gates, and release flow that the whole team can run.
Do we need to standardize on one coding tool?
No. Teams can keep mixed tool usage. The sprint defines shared output standards and workflow contracts regardless of tool preference.
Is this a long consulting retainer?
No. It is a bounded two to four week sprint with explicit deliverables and a handoff plan.
Will you write production code for us?
This is not staff augmentation. I can run pilot execution with your team, but the mandate is to install a repeatable system your team owns.

If that sounds like your current bottleneck, get in touch.

Individual consulting, fixed scope, explicit outcomes.

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