Use case · Inquir Compute

AI agent backend for serverless AI agents

Model tools become HTTP tool routes on the gateway: lock them down with API keys, inject secrets per function, offload long steps to jobs or pipelines, and ship handlers on Node.js, Python, or Go with the same observability as the rest of your stack.

Deploy one authenticated tool →Serverless for AI agents

Workload and what breaks

What agents actually need

A chat completion is only the headline. Production agents also fetch private data, write to systems of record, escalate to humans, and enforce guardrails—and each of those steps needs clear failure and retry semantics.

When there is no real backend, side effects creep into prompt text or the user’s browser, where they are nearly impossible to audit or revoke cleanly.

Where shortcuts fail

Fragile patterns

Running privileged tools on end-user devices breaks the moment data is regulated or the user is on a locked-down laptop.

Throwing every tool into one giant server turns every deploy into a high-risk change and makes log lines impossible to attribute to a specific capability.

How Inquir helps

Composable serverless tools for AI agents

One function per tool keeps dependencies and deploy risk localized; execution history in the console matches each tool invocation—easier on-call than a shared monolith.

Handlers use the same Node.js, Python, or Go runtimes as the rest of the platform. Optional warm pools trim cold-start overhead when the model calls tools in a tight loop—measure under realistic load.

What you get

Production rules for AI agent tools

One tool per function

Split functions unless dependencies are tightly coupled.

Isolate integration secrets

Scope credentials per tool integration to reduce blast radius.

Alert per tool

Track failures per tool endpoint, not only per chat session.

What to do next

How to build AI agent tools on Inquir Compute

Define per-tool input contract

Document required fields and validation behavior.

Define output schema + auth model

Keep return shapes stable and route-level access explicit.

Define retries, idempotency, and timeout handoff

Choose when to retry in-place and when to continue via jobs or pipelines.

Code example

Small tool handler

POST JSON arrives as event.body string. Return plain JSON for internal pipeline/tool calls; return { statusCode, body } for HTTP route responses.

tools/lookup.mjs

export async function handler(event) {
  const id = JSON.parse(event.body || '{}').id;
  const row = await db.findById(id);
  return { statusCode: 200, body: JSON.stringify({ row }) };
}

When it fits

Good fit

When this works

Multi-step agents
Tool access to private data

When to skip it

Stateless single-shot completions with no side effects

FAQ

Should agent tools be separate HTTP functions?

Yes for production: one function per tool (or tight group) keeps dependencies isolated, deploy risk small, and logs attributable—easier than a monolith that mixes user sessions and tool IO.

How do I store secrets for tool calls?

Use Inquir workspace secrets and environment injection so API keys never live in prompts or client bundles; rotate keys independently of model versions.

Streaming responses to the user?

End-user streaming is a gateway concern; many tool-calling stacks still use plain request/response JSON between the orchestrator and each tool because retries and idempotency stay simpler that way.