Workflows

End-to-end workflows for agent-assisted development — from bootstrapping to team onboarding.

Pages

• Agent Debugging — Diagnosing bad agent output
• Claude Code ↔ Copilot CLI: Changelog-Driven Feature Parity — Track which CLI tool shipped each capability first and when the other matched it
• Agent Commit Attribution: Signed Commits and Agent Identity — Configure agents to sign or annotate their commits with verifiable identity metadata so audit trails distinguish agent-generated changes from human-authored ones
• Agent-Driven Greenfield Product Development — Build a new product agent-first by defining roles, decomposing to context-safe tasks, and executing through autonomous agent loops
• Central Repo for Shared Agent Standards — Distribute shared agent skills, instruction files, and coding conventions from a central repository to downstream projects
• Enterprise Skill Marketplace: Distribution, Usage Reporting, and Quality Evals — Scale a shared skill library with MDM distribution, private plugin marketplaces, OTel usage telemetry, and a manual eval cadence for high-traffic skills
• Closed-Loop Agent Training from Tool Schemas — Generate synthetic training data from MCP tool definitions, fine-tune small models to match frontier performance, and re-train incrementally as schemas evolve
• Skill Library Refinement Loops — Four complementary feedback mechanisms that together keep a shared skill library accurate and useful
• Continuous Agent Improvement — Iterating on agent quality over time
• Escape Hatches — Unsticking stuck agents
• Daily-Use Skill Library: Encoding Your Process as Agent Skills — Build a small library of purpose-built skills that encode your engineering process from ideation through architecture
• SDLC-Phase Skill Taxonomy: Full-Lifecycle Skill Libraries — Organize a skill library around SDLC phases so phase-entry commands activate only the relevant skills at each stage
• Introspective Skill Generation — Mine agent patterns across sessions to generate new skills, agents, and hooks
• Eval-Driven Development: Write Evals Before Building Agent Features — Define evaluation tasks and success criteria before implementing agent features to make "done" objective and prevent requirement drift
• Getting Started: Setting Up Your Instruction File — Create and iterate on your first CLAUDE.md, AGENTS.md, or copilot-instructions.md in under thirty minutes
• Google Search Console Monitoring Workflow — Automate GSC and Bing WMT verification, index coverage tracking, and weekly CWV + query reports via API
• Evaluation-Driven Development for Agent Tools — Build agent tools in a prototype-evaluate-analyze-iterate loop rather than optimizing in the dark
• LLM-as-Judge Evaluation with Human Spot-Checking — Combine automated LLM rubric scoring with targeted human review to evaluate multi-agent output at scale without sacrificing quality on edge cases
• Continuous Autonomous Task Loop — Self-directed agent loop that selects, executes, commits, and iterates over a task backlog with fresh context per task and rate-limit handling
• Factory Over Assistant: Orchestrating Parallel Agent Fleets — Shift from watching one agent to orchestrating parallel agents with automated feedback loops — and the infrastructure required to make it viable
• Parallel Agent Sessions Shift the Bottleneck from Writing Code to Making Decisions — Running multiple simultaneous agent sessions transforms the engineer's role from contributor to tech lead
• QA Session to Issues Pipeline — Multi-stage agent pipeline that transforms raw QA session documents into investigated, context-rich GitHub issues via codebase investigation
• Backlog Triage as a Named Agent Skill — A single skill encodes a state machine into issue labels and produces a durable agent brief as the executor hand-off contract
• Auto-Triage Workflow: Bug-Monitoring Agent that Connects Related Reports and Opens Fix PRs — Four-stage agent workflow (monitor, correlate, investigate, propose-fix) that watches alert streams and either tags the right owner or opens a fix PR — only safe under three named preconditions
• Throwaway-Prototype Skill: Build to Discard, Keep Only the Answer — A scoped skill that builds the smallest end-to-end thing to resolve one design question, forbids polish, and captures only the verdict before the code is deleted
• PostToolUse Hooks: Automatic Formatting and Linting After Every File Edit — Configure a PostToolUse hook so that formatting and linting run automatically after every file Claude writes or edits
• Repository Bootstrap Checklist — Adding agent support to an existing repo
• Experiential-Learning Setup Agents with Snapshot Rollback (SetupX) — Capture dual-modality fix records, trial them under Docker snapshot rollback, and verify with prosecutor-judge — when prebuilt environments are not an option
• Simulation and Replay Testing for Agent Workflows — Validate agent prompt changes by replaying a past task in isolation and diffing the result against what was actually merged
• Single-Branch Git for Agent Swarms — At 10+ parallel agents, feature branches cause merge conflicts and waste context on rebases; single-branch with advisory reservations and mechanical guards is the alternative
• Single-CLI Agent Platform: Create to Production in One CLI — Bundle scaffold, run, eval, deploy, and publish into one CLI when the team is on a single cloud and wants the agent itself to self-serve its own deploys
• Sparse-Checkout Worktrees for Monorepo Agent Isolation — Use worktree.sparsePaths to limit an agent's file-system view to one service subtree, reducing context noise and accidental blast radius in large monorepos
• Lazy Worktree Isolation: Enter the Worktree on First Write, Not on Dispatch — Background sessions start in the parent checkout and only relocate into an isolated git worktree on the first Edit or Write tool call
• Team Onboarding for Agent Workflows — Bringing a team up to speed on agent-assisted development
• The AI Development Maturity Model — Phases of AI coding tool adoption, from skeptic to agent-native workflows
• Plan Mode: Read-Only Exploration Before Implementation — Restrict agents to read-only operations to surface understanding and correct approach before any code is written
• The Research-Plan-Implement Pattern — Structure agent work in three explicit phases to avoid context-wasteful rework from premature coding
• Mise en Place for Agentic Coding — Three-phase preparation methodology (contextual grounding, collaborative specification, task decomposition) that front-loads alignment work before parallel agent fan-out
• The Plan-First Loop: Design Before Code — Summarize, correct, plan, and approve before the agent writes a single line of implementation code
• Cloud Planning with Inline-Comment Review and Execute-Anywhere Choice — Generate the plan in a cloud session, review it inline in the browser, then defer the cloud-versus-local execution choice to approval time
• The Velocity-Quality Asymmetry: Why AI Speed Gains Fade Without QA Investment — Empirical evidence shows AI coding tools produce transient velocity gains but persistent quality degradation — sustainable speed requires scaling QA as a first-class concern
• AI Slop as a Process Problem: Encoding Quality Standards as Pipeline Gates — Treat AI-generated slop the way CI/CD treats broken builds — a per-PR agent gate enforcing version-controlled standards, sized to agent throughput rather than reviewer attention
• Vibe Coding: Outcome-Oriented Agent-Assisted Development — Delegate implementation entirely to the agent and focus on evaluating outcomes for low-risk, throwaway work
• Agent Observability in Practice: OTel, Cost Tracking, and Trajectory Logging — Wire up OpenTelemetry on Claude Code and LangChain agents for cost dashboards, compliance audit trails, and trajectory debugging
• Prototype Before Optimizing: Establish Quality Baselines Before Token Constraints — Defer production efficiency constraints until after establishing quality baselines to avoid locking in suboptimal architectures
• Token-Cost Profiling and Reduction for Always-On Agentic Workflows — Instrument-attribute-fix-verify loop that treats per-PR and per-CI workflows as a measurable cost surface, with named levers (MCP pruning, pre-agentic CLI, relevance gating, cheaper-model routing, cache reuse) and frequency-weighted preconditions
• Background-to-Foreground Handoff — Transfer work from a background agent to a human at the ~90% completion mark using distilled summaries and artifact-based handoff points
• In-Thread Side-Channel — Ask a mid-task clarifying question inside one session using a tagged sub-conversation that returns the agent to its prior goal
• Canary Rollout for Agent Policy Changes — Gate agent policy updates behind a traffic-split rollout so regressions surface on a small blast radius before full deployment
• Burn the Boats — Commitment-Forcing Deprecation — Remove a working feature entirely with a hard deadline to force full commitment to a new paradigm and stop anchoring to obsolete approaches
• The 7 Phases of AI-Assisted Feature Development — Feature-level workflow model with entry and exit criteria: Research, Prototype, PRD, Issues, Implement, QA, Ship
• Monolith-to-Sub-Agents Refactor — Five-step migration checklist for taking a brittle monolithic agent prototype to an orchestrated pipeline with schema-first outputs, dynamic RAG, tracing, and framework-native circuit breakers
• Chat-Platform Agent Delegation — Mention a cloud coding agent in a Slack or Microsoft Teams channel to delegate from where coordination already happens, with a concentrated lethal-trifecta posture that the IDE entry point hides
• Public-Channel Agent Work as Lehrwerkstatt — Force agent conversations into public channels so the team learns from every transcript — a high-yield organizational practice with hard psychological-safety and data-scope preconditions
• Stakeholder Trust Through Evals and Observability — Three-artefact stakeholder review cycle — dashboards, custom trace views, ad-hoc query — that transfers eval and observability data to non-engineers when paired with narrated error analysis, plural headline metrics, and an in-workflow surface
• Parallel Polyglot Ports as a Spec-Ambiguity Oracle — Generate multiple AI-paired ports from one executable spec and treat divergence as a signal that the spec under-specifies behaviour — not as a fault-tolerance vote

Last reviewed: 2026-05-27