Agentic Coding: Agent Loops
Agentic Coding: Agent Loops - THE LGTM
Agentic Coding: Agent Loops
The most important shift in 2026 is the emergence of long-running autonomous workflows. Instead of responding to a single prompt, agents now operate through execution loops — plan, code, test, fix, repeat.
Last Updated: April 5, 2026
The Agent Loop Architecture
At its core, an agent loop is simple:
- Plan: Analyze the task and create a strategy
- Execute: Write code, run commands, make changes
- Verify: Run tests, check outputs, validate results
- Reflect: Compare results to expectations
- Iterate: Fix issues, adjust approach, try again
The magic isn't in any single step — it's in the loop itself. Failed tool calls, empty results, ambiguous responses all trigger revised strategies. The model has visibility into downstream outcomes and can self-correct.
Why Loops Matter
Without iteration, agents are just chatbots:
- ❌ Cannot recover from failure
- ❌ No visibility into downstream outcomes
- ❌ Single-shot responses to complex problems
- ❌ No adaptation to changing conditions
With loops:
- ✅ Failed tests trigger debugging
- ✅ Type errors trigger fixes
- ✅ Runtime exceptions trigger investigation
- ✅ Ambiguous requirements trigger clarification
Loop Types
1. REPL-Style Loops (Claude Code, Aider)
Interactive back-and-forth. User provides input, agent responds, user provides more input. Best for exploratory work.
Pattern:
User: "Add user authentication"
Agent: Plans, writes code, shows diff
User: "Use JWT instead of sessions"
Agent: Updates code, shows changes
User: "Add password validation"
Agent: Implements, runs tests
2. Background Loops (Cursor Background Agents, GitHub Copilot Coding Agent)
Agent works asynchronously while user does other tasks. Best for parallelizable work.
Pattern:
User: assigns GitHub issue to agent
Agent: checks out branch, implements feature
Agent: runs tests, fixes failures
Agent: creates PR with description
User: reviews when ready
3. Multi-Agent Loops (Google Antigravity, OpenAI Codex Desktop)
Multiple specialized agents working in parallel. Best for complex, multi-faceted tasks.
Pattern:
Planning Agent: breaks down requirements
Frontend Agent: implements UI components
Backend Agent: builds API endpoints
Testing Agent: writes and runs tests
Integration Agent: coordinates, validates
4. Spec-Driven Loops (Kiro)
Structured requirements guide the loop. Best for production software requiring consistency.
Pattern:
Spec: defines requirements, design, tasks
Agent: works through spec methodically
Hooks: validate at each stage
Review: human approves before proceeding
Error Recovery Patterns
Retry with Backoff
Transient failures (network, rate limits) → wait and retry with exponential backoff
Alternative Strategy
Approach failed → try different implementation, library, or algorithm
Escalation
Agent stuck → pause, ask human for direction, preserve context
Rollback
Changes broke something → revert to last known good state, try different approach
Decomposition
Task too complex → break into smaller sub-tasks, tackle individually
Loop Control Mechanisms
Iteration Limits
Prevent infinite loops. Common defaults:
- Simple tasks: 5-10 iterations
- Complex features: 20-50 iterations
- Research tasks: 100+ iterations with checkpoints
Timeout Guards
Maximum time per loop iteration:
- Fast feedback loops: 30 seconds
- Test suites: 5 minutes
- Full builds: 30 minutes
Cost Ceilings
Token or credit limits to prevent runaway costs
Human Checkpoints
Require approval at critical points:
- Before destructive operations (deploy, delete)
- Before committing to main branch
- After significant architectural changes
Designing Effective Loops
Clear Exit Conditions
Define what "done" means:
- All tests pass
- Specific functionality works
- Code review approved
- Performance benchmarks met
Rich Feedback
Each iteration needs signal:
- Test results (pass/fail + output)
- Type checker output
- Linter warnings
- Runtime logs
State Management
Loops need memory:
- What was tried
- What failed and why
- What patterns worked
- Current hypothesis
Context Pruning
Long loops accumulate noise:
- Summarize successful iterations
- Drop irrelevant old attempts
- Keep failure patterns that inform current approach
Common Loop Anti-Patterns
🚩 Oscillation
Agent flips between two approaches, neither working. Fix: Force third alternative or human intervention.
🚩 Depth-First Rabbit Holes
Agent chases one error deep into unrelated code. Fix: Breadth-first exploration, step back periodically.
🚩 Premature Optimization
Agent refactors working code repeatedly. Fix: Clear "good enough" criteria, time-box polishing.
🚩 Confidence Drift
Agent makes increasingly speculative changes. Fix: Validate assumptions, run tests frequently.
🚩 Context Collapse
Long conversation loses track of original goal. Fix: Periodic goal restatement, compact context.
Simon Willison's Unified Logging Pattern
For agents to monitor their own operations and recover from errors:
{
"timestamp": "2026-04-05T09:23:00Z",
"agent_id": "builder-001",
"iteration": 7,
"action": "run_tests",
"input": { "files_changed": ["auth.js", "user.test.js"] },
"output": { "passed": 12, "failed": 2, "duration_ms": 4500 },
"status": "partial_failure",
"next_action": "fix_failing_tests"
}
Structured logs enable:
- Pattern recognition across runs
- Failure analysis
- Progress tracking
- State recovery after crashes
Harness Engineering
The infrastructure that runs agent loops is called a "harness." Key components:
- Sandbox: Isolated execution environment
- Tool Registry: Available capabilities
- State Store: Persistence between iterations
- Event Bus: Communication between components
- Observer: Logging, monitoring, human visibility
- Controller: Loop orchestration and limits
Production harnesses (OpenAI Codex, Claude Code Teams) include:
- Git integration with worktrees
- CI/CD pipeline triggers
- Review queue management
- Audit logging
Loop Performance Tuning
Fast Feedback
Run relevant subset of tests, not full suite:
# Instead of
npm test
# Run only changed
jest --onlyChanged
Parallel Execution
Independent checks run simultaneously:
- Type check + lint + format in parallel
- Unit tests + integration tests split
Smart Caching
Skip work when inputs haven't changed:
- Dependency install (node_modules)
- Build artifacts
- Test results for unchanged files
The Bottom Line
Agent loops are the difference between AI-assisted coding and AI-driven development. The sophistication of your loop architecture determines:
- How autonomous your agents can be
- How much you can trust their outputs
- How much human oversight is required
- How well they handle edge cases
Start simple: A tight feedback loop with good tests beats a complex multi-agent system with poor observability.