Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
Role: AI Agent Systems Architect
I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.
Reason-Act-Observe cycle for step-by-step execution
- Thought: reason about what to do next
- Action: select and invoke a tool
- Observation: process tool result
- Repeat until task complete or stuck
- Include max iteration limits
Plan first, then execute steps
- Planning phase: decompose task into steps
- Execution phase: execute each step
- Replanning: adjust plan based on results
- Separate planner and executor models possible
Dynamic tool discovery and management
- Register tools with schema and examples
- Tool selector picks relevant tools for task
- Lazy loading for expensive tools
- Usage tracking for optimization
| Issue | Severity | Solution | |-------|----------|----------| | Agent loops without iteration limits | critical | Always set limits: | | Vague or incomplete tool descriptions | high | Write complete tool specs: | | Tool errors not surfaced to agent | high | Explicit error handling: | | Storing everything in agent memory | medium | Selective memory: | | Agent has too many tools | medium | Curate tools per task: | | Using multiple agents when one would work | medium | Justify multi-agent: | | Agent internals not logged or traceable | medium | Implement tracing: | | Fragile parsing of agent outputs | medium | Robust output handling: |
Works well with: rag-engineer, prompt-engineer, backend, mcp-builder
Copy a source-pinned command for your client. You run it yourself.
Destination: .claude/skills/ai-agents-architect · pinned to the source commit
# Run from your project root
git clone https://github.com/davila7/claude-code-templates.git .skillboard-tmp
git -C .skillboard-tmp checkout 77b087e2153e35250cd3cc7c192218a2d990b6ed
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/cli-tool/components/skills/ai-research/ai-agents-architect" ".claude/skills/"
rm -rf .skillboard-tmpReview the source before running. This copies files into your project; it is not a one-click install and does not verify runtime safety.
sudo apt update && sudo apt install -y gitnpm install -g @anthropic-ai/claude-code# Run from your project root
git clone https://github.com/davila7/claude-code-templates.git .skillboard-tmp
git -C .skillboard-tmp checkout 77b087e2153e35250cd3cc7c192218a2d990b6ed
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/cli-tool/components/skills/ai-research/ai-agents-architect" ".claude/skills/"
rm -rf .skillboard-tmpDestination: .claude/skills/ai-agents-architect
Scanner static-checks@0.1.0 · commit 77b087e2153e. Static checks cannot prove runtime safety – review the source and the exact diff before installing. How checks work.
No static rules matched. This is not a safety guarantee.