Divisible
Execution splits into targeted streams: migration, testing, refactoring.
Agentic SDLC:
From AI coding tools to reliable delivery systems.
Move from AI experimentation to a delivery model your clients can trust.
Beyond Time & Materials
Beyond Time & Materials
Staffing sold time. The next model sells execution bursts.
Classical team augmentation was built for a world where execution was scarce and projects consumed years. You staffed teams, sold billable days, and scaled with headcount.
With coding agents, productivity rises, delivery compresses, and parallelism increases. Clients can absorb more execution internally, which means people are no longer allocated to projects in the same static way.
External teams do not disappear. But they stop being long staffing commitments. They become short, targeted execution bursts.
Before
people × time
billable days · stable staffing · long projectsNow
execution bursts
parallel streams · short cycles · dynamic allocationClassical T&M5 engineers for 18 months
Programmable execution3 execution streams for 4–6 weeks
What changes for clients
The decision is no longer how many people to staff for how many months.
- How much execution should we inject right now?
- Which initiatives deserve short bursts of acceleration?
- Where do we need parallel streams instead of more billable days?
- When should capacity ramp up, shut down, or restart?
Time-compressed
Work lands in weeks, not in multi-year staffing cycles.
Elastic
Ramp up fast, shut down cleanly, restart when priorities change.
Schedulable
Execution is routed across priorities, not statically assigned to teams.
Projects used to consume billable days. Now they consume execution bursts.
Core shift
Core shift of the developer's role.
As coding agents take on more work, developers do not disappear. Their role changes. The new challenge is supervision: what to delegate, what to review, what to validate, and what not to automate blindly.
What to delegate
Decide where coding agents can move fast without creating hidden operational debt.
What to review
Make supervision explicit so validation, approval, and escalation are built into the workflow.
What not to automate blindly
Protect quality, accountability, and client trust where human judgment still matters most.
Metrics
Without metrics, there is no transformation.
If AI contribution, rework, quality, and supervision are not measured, adoption stays anecdotal and cannot scale.
What must be measured
- AI contribution
Share of work authored by coding agents per sprint
- Rework
PRs that revisit already-reviewed code paths
- Quality
Coverage score, lint pass rate, and test health
- Supervision
Human review coverage across all agent-merged PRs
Measurement over anecdote.
What gets measured gets governed. The factory runs on numbers, not narrative.
Supervision coverage92%
Rework reduction4×
Velocity with control
Agent loops ship faster only when supervision, review, and escalation are measured in the same run.
Today — Sandbox mode
Where developer time actually goes
Most time is consumed by unplanned work and context switching — not delivery.
- Unplanned debugging30%
- Manual coding28%
- Context switching22%
- Documentation12%
- Supervision overhead8%
Target — Factory mode
Metric uplift: sandbox vs factory
What structured supervision and measurement actually moves.
Sandbox
Factory
Who this is for
Built for teams that need AI speed with delivery accountability.
This engagement is for organizations already experimenting with AI coding tools and now looking for a durable operating model.
Engineering leaders
Teams moving from ad-hoc AI usage to governed delivery with measurable outcomes.
Product & platform teams
Organizations standardizing AI-assisted workflows across planning, build, review, and release.
Consultancies & service firms
Delivery groups that need speed gains without sacrificing supervision, quality, or accountability.
Blueprint
AI tool adoption is not a delivery model.
Buying licenses and rolling out coding assistants does not tell a company how to deliver differently. The real challenge is to turn rising productivity and time compression into a new operating model.
As delivery cycles shrink, the allocation of people becomes more dynamic. The question is no longer who sits on a project for 18 months. It becomes: what kind of execution system are we actually building?
Three levels
Agentic SDLC
How software delivery changes
Agentic engineering
How engineers work inside that model
Harness engineering
How agents are made reliable enough to participate
Ground truth first
Separate real adoption from demo culture, isolated prompting, and AI theater.
Prioritize the bottlenecks
Focus on where agentic workflows remove waiting time, handoffs, and rework.
Redesign supervision
Clarify what agents can do alone, what must be reviewed, and where escalation starts.
Instrument the system
Measure AI contribution, throughput, rework, quality, and supervision coverage.
Launch the next operating model
Turn the blueprint into a concrete sprint, pilot, or delivery transformation path.
Engagement
One sprint to define the operating model.
In a focused working sprint, we align leadership and engineering around a practical next step with clear priorities, governance, and measurement.
One focused engagement to define the ground truth, the priorities, and the operating rules that make AI supervision real.
Start the conversation
Still in sandbox mode?
If AI is already entering your delivery workflows, the next step is not more experimentation. It is control, supervision, and measurable execution.