Agentic Engineering Sprint

Turn faster code generation into faster software delivery.

Coding agents make writing software faster, but useful work does not automatically reach production faster. This focused two-week engagement finds friction across the delivery flow and identifies the highest-leverage changes for throughput, quality, and cost.

Talk to me about your current friction

Describe where work is stuck and I'll give you an honest fit assessment.

Format
Focused two-week engagement
Investment
$7,500
Payment
50% upfront · 50% on completion

01 / Delivery problem

Faster code generation is only useful when the rest of delivery can absorb it.

The sprint looks at both sides of the flow: how agent-assisted work is generated and what happens before useful work reaches production.

Find the friction

The sprint covers both sides of the flow: how agent-assisted work is generated and what happens in review, rework, QA, handoffs, team practice, production, and tool spend.

We look for constraints such as PR review, concentrated reviewer responsibility, repeated rework, weak codebase context, inconsistent workflows, or QA. Tripling code generation while review capacity stays flat can merely produce a larger PR queue.

Improve the way agents are used

We examine context, task design, workflows, guardrails, verification, and team practices so generated work fits the delivery process more reliably.

Spend with clearer evidence

We distinguish model and tool spend that appears to create value from disproportionate spend and tokens burned by workflow problems.

02 / What you receive

A baseline, evidence, and one practical next experiment.

Find what prevents faster code generation becoming faster delivery, then give the department a practical way to improve it.

  1. Agentic Delivery Baseline

    A clear view of where the current workflow helps or hurts delivery.

  2. The three highest-leverage changes

    The three highest-leverage changes Ashley would make first.

  3. Evidence behind each recommendation

    The evidence that explains why each change matters.

  4. One 30-day experiment

    A metric, current baseline, and success criteria for one focused experiment.

  5. Leadership findings session

    A session covering the findings and recommended next actions.

03 / During the sprint

A bounded investigation of how your department actually delivers.

  1. Leadership kickoff

    A short leadership kickoff to align on the department and the question to answer.

  2. Developer survey

    A lightweight survey to understand how the team uses coding agents in practice.

  3. Workflow conversations

    Individual conversations with developers about their tools, habits, and delivery friction.

  4. Evidence review

    Examination of department-level delivery and adoption data, recent pull requests, selected projects or repositories, AI usage, guidelines, skills, and other relevant material.

  5. Delivery analysis

    Analysis of codebase context, task design, agent habits, guardrails, verification, review and QA, planning, adoption, and differences across the department.

  6. Findings call

    A final findings call to agree what to change first and how to measure it.

04 / Fit

For leaders who are already using coding agents.

Audience

A CTO, VP Engineering, technical founder, or engineering leader.

Department

The department already uses coding agents in real work and has more than five engineers.

Common signals

  • Review is slowing down
  • Reviewer responsibility is concentrated
  • Bugs are increasing
  • Agent practices vary across the team
  • Trust in generated work is uneven
  • Tool spend is growing

What the buyer wants

You want evidence before purchasing more tools or changing department-wide practice.

05 / Why Ashley

Experienced outside judgment on a real delivery problem.

  • 15 years building and leading engineering teams, including serving as CTO at a 120-person technology startup.
  • Led AI engineering work at Laravel and built Laravel Boost, installed more than 30 million times.
  • Previously led AI work at Remote.com.
  • Building Fuel and working hands-on with agentic software-delivery systems, robust evals, and billions of tokens per day.

06 / Practical details

Small, focused, and explicit about the boundaries.

Scope
One engineering department.
Format
Focused two-week engagement.
Investment
$7,500.
Payment
50% upfront and 50% on completion.
After a fit conversation
Ashley sends a brief engagement summary and invoice. The deposit is credited against the total.

Access and commitments

  • Customer code will not be sent to AI models.
  • Any code access received will be removed after the findings call.
  • If Ashley cannot accept or deliver the engagement, the deposit is refunded in full.

Describe where work is stuck and I'll give you an honest fit assessment.

Talk to me about your current friction

07 / Questions

Before you write.

Could we do this internally?

Possibly. The value is a focused, experienced outside perspective, a bounded investigation, and a concrete experiment. If your team already has the evidence and the people, give them the time instead.

What is included?

The sprint includes:

  • One engineering department
  • Department-level delivery and usage data
  • Selected projects or repositories
  • A lightweight developer survey
  • Individual workflow conversations
  • Analysis
  • A leadership findings call

What is not included?

The sprint does not include:

  • Open-ended implementation
  • Ongoing support
  • Workshops
  • An exhaustive codebase-by-codebase audit
  • Guaranteed productivity outcomes

What access is usually useful?

Read-only access is usually enough. The useful context depends on how your department works:

Usually useful

  • Read-only access to selected codebases and docs
  • PR, review, and merge history
  • Issue tracker or planning board
  • AI usage and cost data, where available
  • Conversations with engineers

When relevant

  • CI/CD and recent build, deploy, or failure history
  • Engineering standards, PR templates, and agent instructions
  • Architecture, ownership, and system-context docs
  • Bug, incident, and error-tracking information

Will our code be sent to AI models?

No. The sprint relies on Ashley's human judgment.

Customer code will not be sent to AI models.

Any code access received will be removed after the findings call.

Can you implement the recommendations?

Not within this sprint. Separate help can be discussed after the findings.

What happens after contact?

We have a 15-minute call about the department, agent use, current friction, and fit.

08 / Next step

If developers already use coding agents but faster generation is not clearly producing faster delivery, identify where the friction moved.

Describe where work is stuck and I'll give you an honest fit assessment.

Talk to me about your current friction