AI Agent Architecture
AI agents are moving from experimental demos to production systems. This pillar page collects my work on agent design patterns, multi-agent orchestration, and the architectural shift from traditional UI to agent-driven interfaces.
The Agent Architecture Shift
In AI Agents as Enterprise UI, I explored how AI agents are replacing traditional application layers in enterprise systems. The core insight: instead of building UI → API → Database, teams are building Agent → Data Layer, with the agent handling the interaction, logic, and presentation that previously required three separate tiers.
Key benefits:
- Reduced infrastructure complexity (fewer layers to build and maintain)
- Natural language interfaces instead of form-based UIs
- Adaptive experiences that learn from user behavior
- Faster iteration — changing agent behavior is faster than redesigning UIs
Key challenges:
- Security and access control must move to the data layer
- Agent reliability and hallucination management
- Cost management for high-volume use cases
- Audit trails for regulated industries
Multi-Agent Systems with CrewAI
Building AI Teams with CrewAI covers my approach to multi-agent orchestration — creating teams of specialized AI agents that collaborate on complex tasks.
The CrewAI Template provides a production-ready starting point:
- Role-based agents — Each agent has a specific expertise and responsibility
- Task orchestration — Agents hand off work in a defined sequence
- Tool integration — Agents can use external tools (search, APIs, databases)
- Output validation — Final output is verified before delivery
When to use multi-agent systems:
- Tasks that naturally decompose into specialist roles (researcher, analyst, writer)
- Workflows where quality benefits from multiple perspectives
- Complex processes with clear handoff points between stages
- Situations where a single agent's context window isn't large enough
When NOT to use them:
- Simple tasks that a single agent handles well
- When latency matters — multi-agent systems are inherently slower
- When cost is a primary concern — each agent consumes tokens independently
Agent-Led Development
AI Agents and the Future of Development captures lessons from a hackathon where our team built an application using agent-led development — where AI agents drive the implementation with human oversight.
Key takeaways:
- Agent-led development works best for well-defined tasks with clear acceptance criteria
- The human role shifts from writing code to defining constraints and reviewing output
- Teams that invested in project context (CLAUDE.md, hooks) were dramatically more productive
- The tools are ready for production work, but team processes need to adapt
The Agent Tooling Ecosystem
My tools support the agent ecosystem:
- PromptConduit — Analytics for understanding how agents (and developers using agents) interact with codebases
- Havoptic — Tracking the rapid release cadence of agent-capable tools
- Claude Code Hooks — Building deterministic automation around agent workflows
- Claude Code Template — Project structure optimized for agent-assisted development