Agentic Enablement · SDLC
Turn AI coding into a governed SDLC.
From scattered assistants to a client-owned harness: standards, gates, and evidence leaders can defend.
For CTOs, VPs of Engineering, and platform leaders accountable for quality, security, and delivery economics.
2–3 weeks
Assessment
10–14 weeks
Transformation
6–12 months
Managed adoption
_> Delivery arc
>> From informal AI usage to a governed harness
Assessment first. Workshops install production artifacts. Embedding proves the pilot. Managed adoption stops drift.
Evidence-led assessment
Maturity scorecard, risk map, pilot thesis, and a clear go / adjust / stop decision.
Install on a live workflow
Shared rules, quality gates, and coached judgment inside a real repository and CI path.
Scale with controls
Prove flow, quality, and economics. Expand only where the harness holds under audit.
Trusted by leaders
>>Licences are easy. Change is hard.
_> Most organizations hit the same three-layer gap after the first wave of enthusiasm.
01
Individual acceleration
Developers prompt faster and automate isolated tasks. This layer is visible and feels productive.
02
Team friction
Inconsistent rules, duplicated prompts, larger PRs, uneven review quality, and hidden rework.
03
Management blind spot
No reliable link between spend, adoption, flow, quality, security, and business outcomes.
>>A client-owned agentic SDLC harness
_> Standards, skills, workflows, guardrails, quality gates, role capability, and management visibility. Models change. The harness stays.
01
Controlled delegation
Risk-adjusted autonomy with human gates for high-risk change, not unbounded agent freedom.
02
Evidence over activity
Flow, quality, control, and economics with baselines agreed during assessment. Every pilot ends in scale, adjust, or stop.
>>For teams that need evidence
_> Strongest fit: active software delivery, an executive sponsor, and a real workflow suitable for an embedded pilot.
Strong fit
- 15–300 engineers or several delivery squads
- Coding assistants already used informally
- Pressure on lead time, quality, or cost
- A CI/testing baseline, or willingness to repair it
- An identifiable pilot workflow
- An accountable executive sponsor
Clarify or no-go
- One-off prompt workshops or generic AI inspiration
- No sponsor, pilot workflow, or repository access
- Expectation that agents remove architecture and review accountability
- Tool procurement as the only approved scope
>>What the client can prove
_> No vanity metrics. Success is measurable delivery improvement with governance that holds.
Flow
01Lead time, cycle time, review wait, and deployment frequency against agreed baselines.
Quality
02First-pass success, escaped defects, change failure rate, and rollback frequency.
Control
03Policy compliance, escalation rate, critical approvals, and auditable agent activity.
Economics
04AI cost per accepted change, engineering hours saved, and cost of rework.
Four-phase transformation
_> Assessment, workshops, embedding, and managed adoption. The default recommendation is the full Transformation path.
→ Scroll to see all steps
>>Engagement shapes
_> Soft timelines only. Commercial bands validated against scope and risk before engagement.
>>Where the harness lives
_> Where the harness lives across the delivery system.
In-editor
Completion, chat, and inline edits with shared rules.
Terminal
Repository-wide tasks, tools, and tests.
Background
Sandboxed tasks that return branches and PRs.
Workflow
CI, review, issue triage, release, and maintenance.
Organization
Shared skills, controls, metrics, and budgets.
Role tracks
Engineers, managers, product, QA/AppSec/platform, and leadership.
Also see
Building AI product features for customers rather than enabling your own delivery system? See Generative AI Solutions.
Generative AI Solutions →