Serverless for AI agents
Workload: model tool calls, background jobs, and LLM pipeline steps that must not live inline in one giant HTTP response. What breaks: secrets in prompts, unbounded retries, and no shared observability with the rest of your APIs. What Inquir gives: each tool as a serverless function behind the API gateway—with auth, secrets off the model path, pipelines, jobs, and Node.js / Python / Go containers.
Workload and what breaks
Why AI agents need a serverless backend
Demos collapse a whole agent into one process. Production needs a serverless backend with authenticated tool calls, rate limits, secrets that never touch the model context, and a clear story when step seven fails and step eight should not run.
Stuffing every side effect into one giant synchronous LLM round-trip does not scale. Small serverless functions with explicit inputs and outputs are easier to test, easier to retry, and easier to explain to security.
Trade-offs
Where lightweight agent stacks break
Notebooks and one-off scripts rarely give you durable deploys, structured logs, and a shared secret model with the rest of your API surface.
A generic cron job on a VM can call a script, but you still own packaging, rollback, and isolation between “low risk housekeeping” and “touches customer money”.
How Inquir helps
What Inquir adds for serverless AI agents
Each tool is a function with a real HTTP contract on the gateway, running in an isolated container—so heavy or untrusted dependencies do not share memory with unrelated features.
Warm pools help when the model calls tools in quick succession; pipelines absorb work that genuinely cannot finish before the gateway times out.
What you get
Core capabilities
Isolated serverless tool execution
Run untrusted or heavy dependencies in separate containers instead of sharing one brittle process.
Authenticated API endpoints for agent tools
Expose typed endpoints the agent can call with predictable auth and routing on the serverless gateway.
Scheduled jobs and async continuation
Trigger periodic sync jobs or kick off background pipelines when user-facing latency matters.
Observability for agent tool runs
Trace executions when a model picked the wrong tool or an upstream API misbehaved.
What to do next
Reference architecture
This is a reference pattern for running AI agents on a serverless backend: tools stay small and synchronous where possible, while pipelines and jobs carry retries, branching, and long-running work without blocking the model.
Orchestrator chooses tool
Your orchestration layer maps the action to a function ID and input payload.
Tool executes with secrets
The runtime injects environment configuration and returns structured JSON to the caller.
Pipeline or job continues work if needed
When work outlasts HTTP, continue with retries, branching, or cleanup using platform orchestration.
Implementation links
Go from architecture to build steps
Start from this serverless-for-agents narrative, then open the guides for concrete handler contracts, tool auth, and operational rules.
When it fits
Best fits
When this works
- Tools that touch private systems.
- Tools with side effects.
- Tools that need retries or logs.
When to skip it
- You only call one third-party API with no isolation or scheduling requirements.
FAQ
FAQ
Do agents have to use HTTP?
HTTP is a simple contract for tools; your orchestrator can wrap local calls during dev and remote calls in production.
How are secrets handled?
Bind secrets to the workspace or function in the product UI. They appear as environment variables at runtime, so API keys never belong in prompts, client bundles, or committed files.
Can I mix languages per tool?
Yes. Different functions can target Node.js, Python, or Go depending on library support.
What about long-running jobs?
Return quickly from the tool’s HTTP handler when you can, then continue with a pipeline or async job so the user-facing path stays responsive and retries stay predictable.
Do I need Kubernetes to run AI agents in production?
No. Inquir runs your tools and workflows as managed serverless functions with gateway routing, containers, and observability—you ship handlers and routes without operating a cluster for this pattern.
Can I run AI agent tools with no cold starts?
Hot containers reduce latency for steady tool traffic, but the first deploy or idle recycle can still be a cold path—plan timeouts and warm pools for the calls that matter most.