AI agent tools API: authenticated serverless endpoints for LLM tools
Give each LLM tool its own authenticated HTTP endpoint behind the gateway: API key auth enforced before the handler runs, secrets injected from the workspace, isolated containers per tool, and warm pools for steady model traffic. One function per tool—small, testable, observable.
Last updated: 2026-04-20
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Direct answer
AI agent tools API: authenticated serverless endpoints for LLM tools. Deploy each tool as a separate serverless function behind the API gateway. The gateway enforces API key auth before the handler code runs—no auth boilerplate per function. Secrets are scoped to the workspace and injected as environment variables at runtime.
When it fits
- Production agents with multiple tools that need auth, isolated secrets, and per-tool observability
- Mixed-language tool backends: Python for ML, Node.js for API calls
Tradeoffs
- One server hosting all tools means one dependency failure (e.g. numpy version conflict) can take down every tool simultaneously. One deploy touches every capability.
- Shared API keys across tools mean rotating one credential affects unrelated tools. A single auth layer for all tools means you cannot scope permissions per tool.
Workload and what breaks
What LLM tool backends need
- Authenticated HTTP endpoints — model cannot call open routes
- Secrets off the model path — API keys never in prompts or context windows
- Isolated execution — one tool failure cannot contaminate another
- Low latency on the warm path — tight tool loops need sub-100ms response
- Observability — trace which tool failed and why, per model call
Most agent demos inline all tool logic in one server or Lambda. When you move to production, you need each tool isolated, each secret scoped, and each failure traceable independently.
Trade-offs
Why monolithic tool backends break in production
One server hosting all tools means one dependency failure (e.g. numpy version conflict) can take down every tool simultaneously. One deploy touches every capability.
Shared API keys across tools mean rotating one credential affects unrelated tools. A single auth layer for all tools means you cannot scope permissions per tool.
How Inquir helps
One function per tool, one gateway for all
Deploy each tool as a separate serverless function behind the API gateway. The gateway enforces API key auth before the handler code runs—no auth boilerplate per function. Secrets are scoped to the workspace and injected as environment variables at runtime.
Hot containers keep tool endpoints responsive for steady agent traffic. Different tools can use different runtimes: Node.js for API calls, Python for ML inference, Go for high-throughput lookups.
What you get
AI agent tools API features
Gateway-level API key auth
Auth happens before your function code runs. No accidentally open tool endpoints. Rotate API keys without touching handler code.
Per-tool secrets
Bind workspace secrets per function. The OpenAI key for the summarizer never reaches the database connector tool.
Isolated containers
Each tool runs in its own container. A broken dependency in one tool does not affect others.
Warm pools for tool loops
Enable hot containers for tools the model calls repeatedly. Measure p95 latency under realistic agent traffic before tuning pool size.
What to do next
How to build an AI agent tools API on Inquir
Define tool contracts
Document input schema, output schema, and error shapes per tool. Keep contracts stable across model versions.
Deploy one function per tool
Node.js, Python, or Go—choose per tool based on library requirements. Bind secrets at the workspace level.
Wire gateway routes with API key auth
Create a route per tool endpoint. Enable API key auth at the route level. Share the API key with your orchestrator via secrets.
Code example
Multi-language tool API: lookup (Node.js) + classify (Python)
Same gateway, different runtimes. The model calls both with the same API key; secrets are scoped per function.
export async function handler(event) { // API key verified at gateway — handler assumes authenticated caller const { customerId } = JSON.parse(event.body || '{}'); if (!customerId) return { statusCode: 400, body: JSON.stringify({ error: 'customerId required' }) }; const record = await db.customers.findById(customerId); // DB_URL from workspace secrets if (!record) return { statusCode: 404, body: JSON.stringify({ error: 'not found' }) }; return { statusCode: 200, body: JSON.stringify({ customer: record }) }; }
import json, os from openai import OpenAI client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) # scoped to this tool only def handler(event, context): body = json.loads(event.get("body") or "{}") text = body.get("text") if not text: return {"statusCode": 400, "body": json.dumps({"error": "text required"})} r = client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": f"Classify intent: {text}"}]) return {"statusCode": 200, "body": json.dumps({"intent": r.choices[0].message.content})}
When it fits
Best fit for AI agent tools API
When this works
- Production agents with multiple tools that need auth, isolated secrets, and per-tool observability
- Mixed-language tool backends: Python for ML, Node.js for API calls
When to skip it
- Single-tool demos that do not need auth or secret isolation
FAQ
FAQ
Can the model discover tool routes automatically?
Return an OpenAPI spec or tool manifest from a discovery endpoint. The gateway routes the model to individual tool functions automatically once wired.
How do I handle tool versioning?
Use path prefixes (/tools/v1/lookup, /tools/v2/lookup) and route both to different function versions. Roll forward when the model is retested against the new contract.