A practical architecture for building AI agents with MCP, including boundaries, observability, and failure handling. AI agents are moving from demos to production systems, and MCP is quickly becoming a common protocol for tool and context integration. This guide covers a practical baseline architecture. Why this matters now As of 2025-2026, MCP support and agent workflows have expanded across major ecosystems, and teams need interoperable patterns rather than provider lock-in. Baseline architecture Orchestrator layer : plans tasks, manages tool calls, and handles retries. Model layer : reasoning/generation model with explicit prompt contracts. MCP tool layer : context servers for docs, repos, tickets, and internal systems. Policy layer : security rules, redaction, and allowed-tool boundaries. Observability layer : traces, token costs, tool latency, failure telemetry. Key design rules Treat MCP servers as untrusted inputs unless explicitly verified. Whitelist to...
Comments
Post a Comment