Serverless for AI agents
AI agents in production need more than a model loop: tool calls reach into private systems, background steps outlast a single HTTP response, and secrets must never ride along in the prompt. Inquir gives you a serverless backend for AI agents where each tool runs as its own function behind one gateway—route-level API-key or bearer auth, env-var secrets that stay off the model path, pipelines for async steps, and isolated Node.js 22, Python 3.12, or Go 1.22 containers.
Last updated: 2026-06-28
Answer first
Direct answer
Serverless for AI agents. Each tool is a function with a real HTTP contract on the gateway, running in its own container—so heavy or untrusted dependencies do not share memory with unrelated features. Each function gets its own env-var config and a 256MB-default memory budget you can raise.
When it fits
- Tools that reach into private systems like databases or internal APIs.
- Tools with side effects you need to gate, authenticate, and trace.
- Tools that need retries, logs, and a per-call execution record.
Tradeoffs
- 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”.
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 its own container—so heavy or untrusted dependencies do not share memory with unrelated features. Each function gets its own env-var config and a 256MB-default memory budget you can raise.
Hot pools (min 1, up to 8 warm containers per function) keep latency low when the model calls tools in quick succession; pipelines absorb work that cannot finish inside a function timeout (5s default, 15min max).
What you get
Common AI agent backend patterns
Tool backend
The model calls small authenticated HTTP functions: /search-customer, /create-invoice, /check-inventory. One function per tool keeps dependencies isolated and deploys low-risk.
Async agent job
The model gets an immediate 200; a pipeline continues enrichment, validation, or notification in the background. Use this when work outlasts the gateway timeout.
Scheduled agent
A cron trigger fires the agent every hour or day to monitor changes, summarize data, or sync external systems — without a persistent long-running process.
Guarded sensitive actions
Gate side effects — sending emails, charging customers, modifying production data — behind a dedicated function with idempotency keys, so a retried or replayed job never double-charges or double-sends.
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.
Code example
Gateway event shape for an agent tool handler
The gateway passes API Gateway-style events: query params, path, and body as a string on POST. Return { statusCode, body } or bare JSON depending on your route configuration.
export async function handler(event) { const { q } = event.queryStringParameters ?? {}; if (!q) return { statusCode: 400, body: JSON.stringify({ error: 'q required' }) }; // API key auth is enforced at the gateway route — handler assumes authenticated caller const rows = await db.searchCustomers(q); return { statusCode: 200, body: JSON.stringify({ rows }) }; }
When it fits
Best fits
When this works
- Tools that reach into private systems like databases or internal APIs.
- Tools with side effects you need to gate, authenticate, and trace.
- Tools that need retries, logs, and a per-call execution record.
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.