Tool Calling

Agentic System Architecture: Engineering Multi-Agent Swarms for Production

Answer-first: Moving AI agents from toy demos to enterprise production requires treating them as Stateful Distributed Systems. This series documents the 6 core pillars of production agentic architecture: Swarm Topology (Router/Worker vs Shared Blackboards), Hierarchical Memory Management, Resilient Tool-Calling Protocols, AgentOps Observability, Automated Evals, and Human-in-the-Loop (HITL) Gateways. 🎯 The Architectural Challenge of Autonomous Agents Building production-ready AI agents is fundamentally a distributed systems engineering challenge, not a prompt engineering trick: ...

Part 3: Resilient Tool Calling — Model Context Protocol (MCP) & Sandboxing

← Previous Chapter: Part 2: Hierarchical Memory | Series Hub | Next Chapter: Part 4: AgentOps & Observability → Answer-first: Standardizing agent tools on the Model Context Protocol (MCP) provides type-safe JSON-RPC contracts, token-budget enforcement, and secure capability boundaries. Code execution tools must run inside isolated WebAssembly (WASI 0.2) or micro-VM sandboxes.

Part 4: Active RAG & Strict Tool Calling: Connecting LLMs to Real-Time Inventory APIs

← Previous Chapter: Part 3: Qdrant Hybrid Search & RRF Optimization | Series Hub | Next Chapter: Part 5: The Self-Reflection Critique Loop → Prerequisite: Read Part 3: Optimizing Qdrant Hybrid Search: Combining Dense, Sparse Vectors & Hard Filters to understand hybrid candidate generation and pre-filtering. Answer-first: Active RAG bridges the gap between static vector embeddings and live warehouse state by executing strict JSON Schema function calls against inventory and dynamic pricing microservices. By orchestrating CloudWeGo Eino tool nodes with Sony gobreaker circuit breakers and dataloader batching, search agents verify SKU stock across 15 regional fulfillment centers in under 4ms without risking downstream cascade outages. ...