Capability 01 of 04 · Development
Coding Agent Usage
How your team uses AI coding assistants - from autocomplete to autonomous agent fleets.
5
maturity levels
15
practices
15
guides
L1 · Stage 01Ad-hoc
Practices - what it looks like3 guides
- Copilot autocompleteHow to use IDE autocomplete as your first step into AI-assisted development.guide→
- Chat in sidebar, ad-hoc questionsHow to use the AI chat panel for one-off code questions and explanations before any systematic workflow exists.guide→
- Agent runs without codebase contextUnderstanding why AI tools at L1 see only what you show them - and why that's the core limitation the entire maturity journey addresses.guide→
Criteria - what to measure
- 01At least one AI coding assistant (Copilot, Cursor, Claude Code) is installed and active for at least one developer
- 02AI autocomplete or chat is used at least once per week by the team
- 03Developers have access to AI chat in their IDE sidebar
- 04Team has experimented with AI-assisted code generation on non-critical tasks
L2 · Stage 02Guided
Practices - what it looks like3 guides
- Agent in IDE with YOLO mode (Cursor 3.6 Run Mode, Windsurf, Claude Code with Opus 4.8)How to use autonomous AI agents with auto-approval enabled to execute multi-file changes without constant confirmation interrupts.guide→
- CLAUDE.md / .cursorrules / AGENTS.md in repoHow to write an agent instruction file that teaches AI tools about your project's conventions, patterns, and constraints - the single highest-leverage action at L2.guide→
- Copilot + Claude Code in parallelHow to use inline autocomplete and an agentic CLI tool simultaneously, each at the granularity it handles best.guide→
Criteria - what to measure
- 01At least one agentic IDE (Cursor, Windsurf, or Claude Code) is used by 50%+ of the team
- 02CLAUDE.md, .cursorrules, or equivalent agent instruction file exists in 100% of active repositories
- 03Agents operate in agentic/YOLO mode (multi-step edits without per-step approval)
- 04Developers use two or more AI tools in parallel (e.g., Copilot + Claude Code)
- 05Agent instruction files are reviewed and updated at least quarterly
L3 · Stage 03Systematic
Practices - what it looks like3 guides
- CLI agents (Claude Code with Opus 4.8 + xhigh effort, Codex, Antigravity CLI) or self-hosted open-weight models (GLM-5.2, Kimi K2.7-Code, DeepSeek V4 via vLLM/llama.cpp) as primaryHow shifting from IDE plugins to CLI-based agents makes AI a programmable, scriptable part of your development workflow rather than a typing assistant.guide→
- Rules files per-team/per-repoHow to evolve from a single project-level CLAUDE.md to a layered system of context files tailored to each team's tech stack and conventions.guide→
- Agent-aware coding conventions (explicit > implicit)How to rewrite your team's coding conventions to be machine-readable - precise, example-driven, and structured for AI consumption rather than human reading.guide→
Criteria - what to measure
- 01CLI agents (Claude Code, Codex) are the primary coding interface for 50%+ of feature work
- 02Per-team or per-repo rules files exist and are maintained with code review
- 03Coding conventions are written as explicit, agent-parseable rules (not implicit tribal knowledge)
- 04Agent usage is tracked per developer and per repository
- 05Agent instruction files follow a standardized template across the organization
L4 · Stage 04OptimizedMost teams aim here
Practices - what it looks like3 guides
- Scheduled / unattended agents + loop engineering: stacked play-test-fix-verify loops (Anthropic Routines, Cursor /loop, OpenLoop, meta-harnesses; Stripe Minions)How to launch AI agents that run to completion autonomously - writing code, running tests, fixing errors, and producing a PR without human supervision.guide→
- Slack/CLI/Web/PagerDuty invocation → PRHow to trigger AI agent tasks from natural language interfaces - a Slack message, a CLI command, a web form - and have the agent autonomously produce a pull request.guide→
- 3-5 parallel agents per developer (Cursor 3.6 Run Mode, Devin MultiDevin, Windsurf)How to shift from sequential AI assistance to managing multiple concurrent agent instances - transforming the developer's role from implementer to orchestrator.guide→
Criteria - what to measure
- 01Unattended agents (Stripe Minions model, Cursor Automations) execute tasks without developer presence
- 02Agents are invocable from at least two channels (Slack, CLI, Web, PagerDuty)
- 03Each developer runs 3-5 parallel agent sessions concurrently
- 04Agent task completion rate without human intervention exceeds 60%
- 05Agent invocation produces a PR within a defined SLA (e.g., under 30 minutes for standard tasks)
L5 · Stage 05Autonomous
Practices - what it looks like3 guides
- Multi-agent orchestration (Claude Code dynamic workflows, Gas Town / custom)How to build systems where specialized agents collaborate - a planner decomposes tasks, workers execute them, and reviewers validate results - to handle complex engineering tasks end-to-end.guide→
- Planner → Worker hierarchyHow to structure a two-tier agent architecture where a planner decomposes engineering tasks and workers execute them in parallel - the canonical L5 pattern for complex autonomous development.guide→
- Hundreds of agents on codebase, 1000+ commits/hThe frontier of AI-assisted development: massive agent parallelization where hundreds of concurrent agents produce thousands of commits per hour on a single codebase.guide→
Criteria - what to measure
- 01Multi-agent orchestration system (planner-worker hierarchy) is in production
- 02Agent fleet sustains 100+ concurrent agents on the codebase
- 03Agent fleet produces 1,000+ commits per week without manual dispatch
- 04Planner agents decompose epics into tasks and assign to worker agents autonomously
- 05Agent fleet self-recovers from failures without human escalation for 90%+ of error cases
From the Field
Recent releases, projects and discussions the AI Radar classified into this capability.
Which level is your team at in Coding Agent Usage?
The criteria above are what we check in a facilitated assessment. Run it yourself to place this capability, see which gates you have passed, and compare it against the other 3 in Development.
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