Capability 03 of 04 · Organization
Team Structure & Roles
How teams are organized and what roles exist to support AI-augmented engineering.
5
maturity levels
16
practices
16
guides
L1 · Stage 01Ad-hoc
Practices - what it looks like3 guides
- Traditional roles: dev, QA, PMThe traditional engineering team organizes work into three primary roles: developers who write code, QA engineers who test it, and product managers who translate business requiremeguide→
- Seniors review and fix AI-generated code; human-skill preservation (reject code you can't understand even if it works)The "senior debugs AI code" anti-pattern emerges when junior and mid-level developers use AI agents to generate code faster than they can verify quality, and the resulting problemsguide→
- AI being evaluated in the team's stack"AI doesn't work in our environment" is the most common organizational statement that blocks progress at L1.guide→
Criteria - what to measure
- 01The team has standard engineering roles (developer, QA, PM)
- 02Senior developers review and fix AI-generated code
- 03Team is open to experimenting with AI-assisted workflows
- 04At least one person informally champions AI tool usage
L2 · Stage 02Guided
Practices - what it looks like3 guides
- AI champion per teamAn AI champion per team is a senior or staff engineer who takes on informal or formal ownership of making AI tooling work for their specific team.guide→
- Context engineer role (initial)Context engineering is the practice of making information legible to AI agents: writing CLAUDE.md files that explain codebase conventions, building MCP server integrations that givguide→
- Training: how to write good prompts/tasksTraining in how to write good prompts and tasks is the L2 investment in the fundamental skill that determines whether AI agents are useful or frustrating.guide→
Criteria - what to measure
- 01AI champion is designated per team with allocated time (not just informal interest)
- 02Context engineer role exists (initial, possibly part-time) for maintaining agent instruction files
- 03Developer training on effective agent interaction (prompt writing, task decomposition) has been conducted
- 04Champion has a regular cadence for sharing learnings across the team
- 05Training materials are documented and available for new hires
L3 · Stage 03Systematic
Practices - what it looks like3 guides
- Platform Engineer (AI tooling); Harness Engineer as the consolidated named skill (Fowler, Weng, QCon track, harness-score L0-L4)The Platform Engineer specializing in AI tooling is the person who builds the infrastructure that makes AI agents effective at scale.guide→
- Context Engineer = full role (now mainstream - dedicated job postings across industry)At L3, context engineering graduates from "something the champion does in their spare time" to a full-time engineering role with its own scope, career path, and organizational standing.guide→
- Review shifts up the lifecycle: from evaluating code to PRD/spec governance; the human owns the outer loop - Quality, Verdict, AnswerabilityWhen most code in a PR is agent-generated, the reviewer's job changes fundamentally.guide→
Criteria - what to measure
- 01Platform Engineer role with AI tooling responsibility exists on the platform team
- 02Context Engineer is a full dedicated role (not part-time, not combined with other duties)
- 03Team's primary activity has shifted from writing code to evaluating and reviewing AI-generated code
- 04Role definitions are updated to reflect AI-augmented responsibilities
- 05Hiring criteria include AI tool proficiency
L4 · Stage 04OptimizedMost teams aim here
Practices - what it looks like4 guides
- Developer = manager of agent fleet (now a product default: Cursor Run Mode, Claude agent view, Antigravity, MultiDevin)At L4, the developer's primary job is not to write code - it's to manage a fleet of AI agents that write code.guide→
- "Keep your Tamagotchi alive" (Yegge); Buddy/Clyde-style gamified observabilitySteve Yegge's "keep your Tamagotchi alive" framing captures a crucial insight about working with AI agents at L4: they are not fire-and-forget automations.guide→
- Span of control = how many agents you can effectively supervise; back pressure: autonomy granted can only equal what you can cheaply verifySpan of control is a management concept from organizational science: how many direct reports can one manager effectively supervise? The answer for human teams is typically 5-9, witguide→
- IPETs (Innovation & Practices Enabling Teams) - Team Topologies pattern for AI stewardship and knowledge diffusionAt L4, the developer's primary job is not to write code - it's to manage a fleet of AI agents that write code.guide→
Criteria - what to measure
- 01Developer role is formally defined as "manager of agent fleet"
- 02Span of control is measured: how many parallel agents each developer effectively supervises
- 03Performance evaluation includes agent supervision effectiveness (not just personal code output)
- 04Span of control target is defined per role (e.g., 3-5 agents for standard developers, 5-10 for senior)
- 05Agent supervision training is part of standard developer onboarding
L5 · Stage 05Autonomous
Practices - what it looks like3 guides
- Agentic Engineer: orchestration + supervision + architectureThe Agentic Engineer is the L5 role that emerges when AI agents become the primary development modality and the human's job is to architect, orchestrate, and supervise rather than implement.guide→
- PEV loop: Plan → Execute → VerifyThe PEV loop - Plan, Execute, Verify - is the fundamental operating model for working with AI agents at high maturity.guide→
- Non-coder contributors via agent interfacesNon-coder contributors via agent interfaces is the L5 capability where product managers, designers, business analysts, domain experts, and other non-engineering professionals can dguide→
Criteria - what to measure
- 01Agentic Engineer role combines orchestration, supervision, and architecture responsibilities
- 02PEV (Plan, Execute, Verify) loop is the standard workflow for all engineering tasks
- 03Non-coder contributors can produce software changes via agent interfaces
- 04Agentic Engineer career ladder exists with defined progression criteria
- 05Non-coder contribution rate is tracked as an organizational capability metric
From the Field
Recent releases, projects and discussions the AI Radar classified into this capability.
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I built a physical Tamagotchi that feeds on my Claude Code activity
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Cybernetics and the “human-on-the-loop” in agentic coding
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A virtual pet companion for your AI — Designed to provide in-context code review feedback with personality. Grow with your buddy and level up together. Works with Clau
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Agentic Workforce Framework, an operating model for autonomous agent teams
article
GM just laid off IT workers to hire those with stronger AI skills
article
Thoughts on GitLab's workforce reduction" and "structural and strategic decisions"
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AI didn't kill your junior pipeline. You did
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Your Future job will be to keep AI on task
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