Advanced practical scenario · Scenario 5

Agent practice

With ADD, multi-agent collaboration, and the Micro-Agent Pattern at the core, master the complete Agent implementation approach from task decomposition and role assignment to engineering governance and delivery acceptance, truly integrating agent collaboration into the team development process.

Learning objectives

Understand the complete ADD process from goal decomposition to delivery acceptance, and organize the collaboration rhythm accordingly
Able to design collaboration relationships among roles such as Planner / Executor / Reviewer
Understand context contracts, permission boundaries, and synchronization mechanisms in multi-Agent systems
Able to identify and govern issues such as context drift, duplicate work, and unclear responsibilities

Methodology

ADD workflow

1
Goal decomposition：First break the goal into verifiable, deliverable task units instead of directly handing vague requirements to a large Agent.
2
Role division：Define input, output, and responsibility boundaries for different roles, such as Planner, Coding Agent, QA Agent, and Reviewer.
3
Context contract：Clearly define what context to share, when to synchronize, and which facts must be verified against code or documentation.
4
Acceptance closed loop：Form a delivery feedback loop through tests, builds, manual checks, and review comments, rather than only checking whether the Agent says it is “done.”

Multi-agent collaboration pattern

Planner / Executor / Reviewer

Suitable for tasks with a clear delivery flow, where the Planner breaks it down, the Executor implements it, and the Reviewer is responsible for quality gates.

Specialist Swarm

Suitable for cross-domain issues such as security, performance, UI, and architecture, with each providing an independent judgment and then the main controller summarizing them.

Micro-Agent Network

Break complex tasks into the smallest responsibility units to improve substitutability, but stricter context management is required.

Human-in-the-loop

Force manual confirmation at critical points such as human approval, releases, and writes to external systems to control high-risk actions.

Engineering governance priorities

  • • Observability: Record each Agent’s inputs, decision summary, outputs, and failure reasons
  • • Boundary control: Define which Agents are read-only, which Agents can write, and which actions must be approved
  • • Deduplication mechanismAvoid multiple Agents doing the same thing through task lists, owners, and dependencies
  • • Cost control: First filter with lightweight roles, then call high-cost models for key decisions

AI tool applications

Use Claude Code to organize collaboration workflows

  • • Use task decomposition and sub-agent division of labor to first clarify who is responsible for exploration, who for implementation, and who for review
  • • Use shared task state to record owner, dependencies, and completion status, reducing context drift
  • • Set up manual confirmation for high-risk actions to avoid “automation false positives”

Review failures with structured logs

Recording task inputs, key decisions, call chains, and failure reasons helps you distinguish whether a problem comes from requirements decomposition, insufficient context, or implementation errors during execution.

Practical case study

Case 1: Multi-Agent code review pipeline

  • • The controlling Agent reads the scope of changes and assigns them to security, performance, and maintainability specialists
  • • Each specialist provides an independent opinion, avoiding the blind spot of “one Agent does it all”
  • • The reviewer summarizes conflicting opinions and gives a conclusion on whether to allow the merge

Case 2: Agent development workflow from requirements to delivery

  • • Planner reads the PRD and code structure, and outputs an executable task list
  • • Coding Agent implements changes according to tasks, QA Agent runs verification, and Reviewer performs the final check
  • • Keep manual approval at stages such as release, database changes, and external system writes

Learning outcomes checklist

Able to break complex goals into independent tasks suitable for agent execution
Able to define clear input, output, and permission boundaries for different agents
Understand the mechanisms of synchronization, review, and escalation in multi-agent collaboration
Know how to govern failure modes using logs, task status, and review gates
Able to distinguish the different teaching objectives of the "Core Skills" page and the "Advanced Practice" page