| name | ai-agent-integration |
| description | Advanced skill for implementing Large Language Model (LLM) features into applications, covering tool calling, Retrieval-Augmented Generation (RAG), prompt caching, and context management. |
AI Agent Integration Patterns
This skill file documents best practices for integrating autonomous agents, chatbots, and generative AI features into enterprise architectures (2026 specs).
1. Tool Calling (Function Calling)
When implementing tool calling for AI models (e.g., using openai, anthropic, or standard LangChain wrappers):
- Deterministic Names: Use clear, hyphenated or snake_case tool names (e.g.,
get_weather_data).
- Strict Zod/Pydantic Schemas: Always back tool parameters with explicit schema validation. Do not trust the LLM to output perfect JSON.
- Fail Gracefully: Inform the LLM if a tool call fails. Provide the stack trace or a summary back as the tool output message so the model can auto-correct.
2. RAG Architectures (Retrieval-Augmented Generation)
- Vector Stores: Favor standalone vector DBs (like Pinecone, Weaviate) or Postgres +
pgvector for embedding storage.
- Chunking Strategy: Chunk documents semantically (by headers or paragraphs), not blindly by character count. Add metadata (e.g., source file, author) to chunks for filtering.
- Hybrid Search: Always implement a hybrid search approach (Dense Vector + BM25 Lexical search) to retrieve both high-level semantic matches and exact keyword matches.
3. Context & Prompt Management
- Prompt Caching: Optimize Anthropic or OpenAI API usage natively by anchoring static system prompts. Organize messages sequentially without injecting dynamic timestamps frequently into system instructions to prevent cache eviction.
- Sliding Windows: Maintain a bounded conversation history. Discard messages older than a specific turn threshold or token limit and inject an AI-generated summary back into the context.
4. Multi-Agent Orchestration
- When architecting multiple agents (like the
.opencode architecture), define strict boundaries. Use a "Project Planner" agent to create an execution plan, and specialized agents limited in scope to execute individual steps.
