Review of current AI use
Understand how AI coding tools are already used across teams, where value is emerging, and where risks appear.
AI is changing software development. The organization must adapt.
We help engineering teams put AI to work: training people, defining responsibilities, delegating to agents and strengthening review. The goal is reliable software, clear accountability and measurable results.
For engineering leaders · platform teams · product organizations · software delivery teams
Swipe left for mission-critical delivery →
↻ Tap either diagram to open a rotated view. Pinch to zoom in and out.
The core gap
Copilot licenses are not an AI strategy.
Becoming AI‑first starts with individual experimentation, grows through shared practices and communities, and reshapes how work is organized. People discover better ways to work with AI, help others adopt them, and redesign workflows around human judgment and AI capabilities.
Becoming an AI-first organization
Experiment widely. Codify what works. Redesign how work gets done.
Explore AI across real work, identify the practices that create value, spread them through the organization, and redesign workflows around what humans and AI each do best.
Swipe to explore the three stages →
- 01
Explore
Discover AI-first ways of working
Build the habit of asking: “How would I do this with AI first?”
Explore planning, analysis, writing, coding, testing, documentation, research, coordination and decision preparation.
- Who
- Individuals · Teams · Early adopters
- How
- Try AI across real work · Compare human-first and AI-first approaches · Share discoveries
- Evidence
- Tasks transformed · Time & quality gains · New workflows discovered · Failure modes
- Controls
- Approved tools · Data boundaries · Safe experimentation
- 02
Codify
Turn discoveries into shared practices
Identify what works, make it teachable, and spread it through the community.
Capture effective workflows, prompts and agent patterns. Turn personal discoveries into organizational knowledge.
- Who
- Practitioners · AI ambassadors · Managers · Domain experts
- How
- Document patterns · Create playbooks · Run peer demos · Develop ambassadors · Coach others
- Evidence
- Practices reused by others · Adoption beyond the original user · Consistent quality · Learning velocity
- Controls
- Peer review · Named owners · Practice standards · Appropriate human verification
- 03
Redesign
Make AI-first the default operating model
Reorganize workflows around what humans and AI each do best.
Update handoffs, responsibilities and review mechanisms. Automate selected steps where appropriate.
- Who
- Team leads · Business owners · Technical owners · Transformation leaders
- How
- Redesign workflows · Reallocate human/AI tasks · Update roles · Embed proven practices
- Evidence
- Adoption across teams · End-to-end cycle time · Quality · Capacity created · Business impact
- Controls
- Accountability · Quality gates · Monitoring · Escalation · Recovery
Community & learning throughout. Share discoveries · Build playbooks · Develop ambassadors · Coach peers
Governance throughout. Controls grow with impact, autonomy and reliance.
Don’t scale automations. Scale new ways of working.
AI-first becomes an organizational capability when discoveries turn into shared practices — and shared practices reshape how work is organized.
BEFORE YOU SCALE AI, CAN YOUR TEAM ANSWER THESE?
01 / 06Delegation
What should engineers delegate to AI?
Can your team answer this today?
Scroll to explore, or choose a question above.
Agentic SDLC closes the gap between individual AI usage and reliable software delivery.
The offer
One sprint to define how your teams will work with AI.
We work with your engineering, product and platform teams to map current AI use, assess the opportunities and risks, define responsibilities and controls, and prepare your first pilots.
You leave with a practical plan for the next 30, 60 and 90 days.
AI maturity assessment
Position teams on a practical maturity scale, from ad-hoc usage to orchestrated agentic workflows.
Roles, workflows and responsibilities
Define how AI-assisted delivery should work across roles, workflows, supervision, review, quality, and governance.
Training for key roles
Align engineers, tech leads, product managers, QA, platform teams, and delivery managers on how work changes.
Metrics and reporting dashboard
Define the signals needed to measure adoption, AI contribution, quality, rework, velocity, cost, and team confidence.
30/60/90-day roadmap
Leave with a pragmatic implementation plan and the first pilots to launch.
Sprint outcome: your Agentic SDLC blueprint
AI Engineering Maturity Scale
Where does your team stand?
Self-Assessment for Executives5 stages from ad-hoc AI usage to full fleet orchestration. Click any level to explore what it means and how to advance.
L2selected level
Key takeaway: AI maturity is not defined by the tools you buy, but by the autonomy of your CI/CD pipelines and the discipline of your supervision model.
Agentic engineering curriculumClaude Partner NetworkMember · Get certified by Anthropic
Working with coding agents: the engineering skills that matter
Five practical modules on specifying tasks, equipping agents, checking their work and coordinating delivery.
Five practical modules · Build reliable agentic engineering habits
Module 1: Master the agent workflow
- Experiment with agents across analysis, coding, debugging, and documentation.
- Understand planning, tool use, context limits, and feedback loops.
- Recognize when to steer, restart, or take over.
Practice: Complete a familiar engineering task with an agent and examine where your intervention mattered.
Explore
Module 2: Specify and delegate
- Define the objective, constraints, invariants, and acceptance criteria.
- Break work into bounded, independently verifiable changes.
- Surface assumptions and resolve uncertainty before execution.
Practice: Turn an ambiguous request into an executable task brief.
Specify
Module 3: Equip the agent
- Prepare repository instructions, architectural context, and useful examples.
- Make tools, builds, tests, and diagnostics accessible.
- Set permissions, execution boundaries, and stopping rules.
Practice: Prepare a real repository for repeatable agent-assisted development.
Equip
Module 4: Verify and debug
- Review behavior, architecture, and the actual changes.
- Challenge agent-written tests against requirements and known failure cases.
- Investigate regressions, edge cases, and performance issues before accepting the result.
Practice: Find a deliberately introduced defect and produce a reviewed change with validation evidence.
Verify
Module 5: Orchestrate and own delivery
- Coordinate agents where tasks can be separated, and manage integration.
- Establish review gates, escalation, release checks, and recovery.
- Measure completed work, review effort, rework, time, and cost.
Practice: Deliver a bounded change through a repeatable workflow your team can reuse.
Orchestrate
Module 5 builds on the foundations established in Modules 1–4
Finish with a working software change, evidence that it meets the requirements and a workflow your team can reuse.
Classify the task, not the person.
Apply the right level of discipline based on the consequences of failure.
Internal tools can carry high risk.
Internal systems can still impact people, data, operations and reputation.
More agents ≠ stronger assurance.
Assurance comes from discipline, controls and accountability, not agent count.
Autonomy earns trust through evidence, enforced controls and human accountability.
Inside the agent toolkit
Claude Agent SDK, slide by slide
Explore the loop, tools, hooks and guardrails behind an agent. Select a slide to take a closer look.
13 slides · Original deck in EnglishMetrics
Is AI actually improving software delivery?
We combine delivery data with quarterly feedback from your teams to track quality, lead time, rework, review effort and cost. Together, these measures show where AI helps, where it creates extra work and what needs to change.
What the System Produces
Five groups of metrics track AI use, software quality, delivery performance, agent behavior and cost throughout the development workflow.
What people experience
A quarterly survey combines a shared set of questions with questions tailored to each respondent’s role.
Common baselineFor every respondent
Calibrates role, engagement model, AI usage frequency, autonomy level, and learning posture — the context every other answer is read against.
The questionnaire is proprietary and is provided as part of the engagement.
Next steps
From first sprint to scaled adoption
The first sprint creates the blueprint. The next step is implementation through pilots, training, and operating metrics.
After the initial sprint, organizations can move into targeted adoption programs: team pilots, role-based training, workflow redesign, measurement, and governance support.
Launch a team pilot
Select one or two engineering teams and redesign their delivery workflow around AI-assisted execution.
Role-based training
Train developers, tech leads, product managers, QA, platform teams, and delivery managers on how their work changes.
Workflow redesign
Define repeatable patterns for specification, coding, testing, review, documentation, migration, refactoring, and maintenance.
Platform and tooling alignment
Connect AI coding tools with repositories, documentation, CI/CD, policy checks, and internal engineering standards.
Set up measurement
Track adoption, quality, velocity, AI contribution, rework, cost, and team confidence.
Governance and supervision
Create practical rules for what AI can do, what humans must review, and how accountability is preserved.
Audience
Who Agentic SDLC is for
Built for organizations that need AI speed with delivery accountability.
Engineering leaders
You need to understand whether AI coding tools are creating real productivity, where risks are emerging, and how to scale adoption safely.
Platform and tooling teams
You need to integrate AI tools into the engineering environment: repositories, CI/CD, documentation, identity, policies, and internal standards.
Product and delivery leaders
You need to understand how AI changes planning, estimation, review, quality, and delivery predictability.
Software delivery organizations
You need to prepare for a shift from staffing-based delivery to measurable AI-enabled execution.
Start the conversation
Where could AI make a difference in your engineering teams?
Start with a conversation about your current practices and priorities: skills, workflows, quality or measurement.
No generic AI evangelism. No tool-only training. The focus is delivery: workflows, quality, supervision, metrics, and adoption.