Claude Code Agents: Build Autonomous AI Development Workflows
Manas Takalpati
Founder, Blue Orchid
Claude Code is not just a chatbot that writes code. It's an agent - a system that observes, thinks, acts, and iterates toward goals. Understanding this agent architecture lets you build more sophisticated development workflows.
The Agent Model
Claude Code follows the classic agent loop:
- Observe - Read files, check git status, analyze errors
- Plan - Decide what to do (plan mode makes this explicit)
- Act - Write files, run commands, call tools
- Evaluate - Check results (did the build pass? tests green?)
- Iterate - Adjust approach based on results
This loop runs continuously until the task is complete or you intervene.
Multi-Agent Patterns
Subagent Delegation
Spawn subagents for parallel work. The main agent coordinates while subagents handle exploration, research, and focused implementation.
Background Agents
Run agents in the background for long-running tasks. Monitor progress and intervene only when needed.
Pipeline Agents
Chain agents where each one's output feeds the next: research → plan → implement → test → review.
Permission and Safety
Claude Code has a permission system controlling what agents can do:
- Allow lists - Tools the agent can use freely
- Deny lists - Dangerous operations that are blocked
- Approval prompts - Actions requiring your explicit consent
Configure in .claude/settings.json to balance autonomy with safety.
Real-World Agent Workflows
Feature development: Agent receives a feature spec → enters plan mode → you approve → it implements across multiple files → runs tests → reports results.
Code review: Agent reviews a PR → checks for security, performance, patterns → generates a review comment with actionable findings.
Content generation: Agent researches a topic → generates content following templates → validates against quality criteria → saves results.
Understanding how AI agents work at a conceptual level helps you design better Claude Code workflows.
Frequently Asked Questions
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