Autonomous Agent Chains with Devin

The Rise of Autonomous Agents

Devin 2.0 represents a new paradigm: an AI that can work independently on complex tasks for extended periods. Unlike interactive assistants, autonomous agents:

• Plan multi-step approaches independently
• Navigate web resources for research
• Execute shell commands and manage environments
• Iterate on solutions without constant guidance

When to Deploy Autonomous Agents

Ideal scenarios:

• Feature implementation with clear requirements
• Research-heavy tasks (API integrations, library evaluation)
• Repetitive refactoring across many files
• Documentation generation from code
• Test suite creation

Avoid for:

• Decisions requiring business context
• Security-sensitive operations
• Customer-facing UX decisions
• Architecture changes

Setting Up Devin Integration

Define Clear Boundaries

<!-- .claude/devin-tasks.md -->

# Devin Task Guidelines

## Approved Task Types
- ✅ Implement CRUD operations for defined models
- ✅ Add unit tests for existing functions
- ✅ Integrate third-party APIs with provided documentation
- ✅ Refactor code following established patterns
- ✅ Generate TypeScript types from JSON schemas

## Restricted Tasks
- ❌ Database schema changes
- ❌ Authentication/authorization modifications
- ❌ Deployment configuration changes
- ❌ Dependency major version upgrades

Create Task Templates

<!-- .claude/templates/devin-task.md -->

# Devin Task: [Task Name]

## Objective
[Clear, single-sentence goal]

## Context
- Repository: [repo-url]
- Branch: [branch-name]
- Related Files: [list key files]

## Requirements
1. [Specific requirement 1]
2. [Specific requirement 2]
3. [Specific requirement 3]

## Constraints
- Follow existing code patterns in [reference-file]
- Use [specific library] for [purpose]
- Do NOT modify [protected files/directories]

## Success Criteria
- [ ] All tests pass
- [ ] No TypeScript errors
- [ ] PR created with clear description
- [ ] [Specific acceptance criteria]

## Example Reference
[Link to similar implementation or code sample]

Agent Chain Architecture

The Handoff Chain

Claude Code ─── Plans & Delegates ───→ Devin
                                         │
                                         ▼
                                    Works Autonomously
                                    (30 min - 4 hours)
                                         │
                                         ▼
Claude Code ◄─── Creates PR ────────────┘
     │
     ▼
Reviews & Refines
     │
     ▼
Cursor ◄─── Final Polish ───────────────┘

Practical Chain Example

Step 1: Claude Code Analysis

claude "Analyze our API structure and create a Devin task
for adding a new /api/analytics endpoint. Include:
- Existing patterns to follow
- Required database queries
- Expected response format
- Test requirements"

Step 2: Dispatch to Devin

Claude Code generates the task specification:

# Devin Task: Analytics API Endpoint

## Objective
Create a new analytics endpoint that returns user engagement metrics.

## Context
- Repository: github.com/company/app
- Branch: feature/analytics-api
- Related Files:
  - src/api/users/route.ts (pattern reference)
  - src/lib/db/queries.ts (database utilities)
  - src/types/api.ts (response types)

## Requirements
1. Create GET /api/analytics endpoint
2. Accept query params: startDate, endDate, userId (optional)
3. Return aggregated metrics: pageViews, sessions, avgDuration
4. Include proper error handling for invalid dates

## Constraints
- Follow the pattern in src/api/users/route.ts
- Use existing db.query() utility
- Add types to src/types/api.ts
- Do NOT modify existing endpoints

## Success Criteria
- [ ] Endpoint returns correct data format
- [ ] Query parameter validation works
- [ ] Unit tests cover happy path and edge cases
- [ ] TypeScript compiles without errors

Step 3: Monitor Progress

While Devin works, Claude Code can:

• Monitor the branch for commits
• Review incremental changes
• Prepare follow-up tasks

# Claude Code monitors
watch -n 60 'gh pr list --head feature/analytics-api'

Step 4: Review and Integration

claude "Review Devin's PR for feature/analytics-api:
- Check code quality and patterns
- Verify test coverage
- Identify any security concerns
- Suggest improvements if needed"

Chain Coordination Patterns

Sequential Processing

# Pseudo-workflow
tasks = [
    ("claude-code", "analyze_and_plan"),
    ("devin", "implement_feature"),
    ("claude-code", "review_code"),
    ("cursor", "polish_and_fix"),
    ("claude-code", "final_review_and_merge")
]

for agent, task in tasks:
    result = dispatch(agent, task)
    if not result.success:
        escalate_to_human(result)

Parallel Workstreams

Claude Code Plans
       │
       ├──────────────────────────┐
       ▼                          ▼
    Devin                      Devin
  (Backend API)            (Frontend UI)
       │                          │
       └──────────┬───────────────┘
                  ▼
           Claude Code
      (Integration Review)

Error Recovery

When an autonomous agent gets stuck:

Timeout Handling

# If Devin hasn't committed in 2 hours
claude "Devin's analytics task appears stalled.
Check the branch status and either:
1. Provide additional guidance
2. Break down the task into smaller pieces
3. Take over the implementation"

Rollback Protocol

# If agent produces problematic code
git checkout main
git branch -D feature/analytics-api
git push origin --delete feature/analytics-api

claude "The autonomous implementation had issues.
Let's take a different approach: [new strategy]"

Best Practices for Agent Chains

1. Clear Success Criteria: Define exactly what "done" looks like
2. Time Boxing: Set maximum durations for autonomous work
3. Incremental Commits: Request frequent commits for visibility
4. Escape Hatches: Define when to escalate to human
5. Context Preservation: Document decisions for chain continuity

Measuring Chain Effectiveness

Track these metrics:

• Task completion rate
• Time from dispatch to PR
• Number of revision cycles
• Code quality scores
• Test coverage achieved

Next Module Preview

In Module 2, we'll apply these multi-agent techniques to complex project architecture, learning how to coordinate agents for building full features across the stack. :::