AI Agent API
An AI agent API is an HTTP or streaming interface that runs autonomous or semi-autonomous workflows: interpreting goals, planning steps, calling tools or other APIs, and returning structured results. Agent APIs often loop between a language model and tool handlers until a stop condition is met.
Technical Architecture & Deep-Dive
Chat completion APIs return one model message. Agent APIs orchestrate multiple model turns, memory, and side effects such as creating tickets or querying databases. Clients may subscribe to progress events over server-sent events or WebSockets.
Unbounded agent loops can exhaust model budgets and leak credentials if tools wrap raw SaaS keys. An LLM gateway plus edge BFF pattern keeps tool secrets server-side, caps iterations, and slim streaming payloads for mobile clients.
Key Architectural Benefits
Build a Governed Agent Endpoint
Chain model calls, tools, and fallbacks at the edge with rate limits and secret injection.
Frequently Asked Questions
Q:How is an agent API different from a chat API?
Chat APIs focus on conversational messages. Agent APIs add planning, tool execution, and persistence across turns.
Related Architecture Concepts
An LLM gateway, also called an AI gateway, is a proxy layer between applications and large language model providers such as OpenAI and Anthropic. It injects API keys server-side, enforces rate limits and budgets, caches responses, logs usage, and can fail over between models.
Model Context Protocol is an open standard for connecting AI applications to external tools, data sources, and prompts through a uniform client-server interface. MCP servers expose callable tools and readable resources so language models can retrieve context and act through governed APIs.
LLM tool calling is when a language model outputs structured instructions to invoke defined functions or HTTP tools instead of only natural language. The host runtime parses tool calls, executes them against approved backends, and feeds results back into the model context for another turn.
API orchestration is the coordination of multiple API calls in a defined sequence, where outputs from one step feed inputs to the next. An orchestrator manages ordering, dependencies, error handling, and the final response, so clients trigger a multi-step workflow with a single request.
Ready-to-Use Edge Recipes
To combine anthropic claude with openai fallback router, use one RealComposer pipeline with parallel steps. Primary Anthropic Messages step plus OpenAI fallback step in one pipeline. Your Frontend / Web app makes one request instead of several sequential upstream calls.
Anthropic payloads are heavy for Frontend / Web. Edge strips rate limit headers and debug from streamed JSON chunks. RealComposer output mapping returns only the fields your UI needs at the edge.
To solve secure anthropic claude api proxy for frontend, never execute unauthenticated requests or embed private tokens directly in client bundles. RealComposer acts as a sub-10ms edge gateway running on Cloudflare Workers that securely injects credentials from your Secret Vault, normalizes responses, and prevents unauthorized API usage. Route calls through your RealComposer edge URL to guarantee complete privacy, robust origin validation, and low mobile latency.