Serverless use cases for AI agents, cron jobs, webhooks, and APIs
Pick the workload you ship next: serverless AI agents, serverless cron jobs, webhook processors, background jobs, or REST API endpoints—each guide ties HTTP ingress, secrets, jobs, and pipelines on Inquir Compute to one coherent pattern.
Start here
Choose your guide by outcome
Jump to the playbook that matches what you ship this sprint—serverless AI agents, serverless cron jobs, webhook processors, background jobs, or REST API endpoints—with architecture notes, failure modes, and baselines.
Workload and what breaks
Feature lists are useful, but delivery starts from outcomes
Teams lead with outcomes: how to acknowledge webhook processors before heavy work, how to ship serverless AI agents without leaking secrets, how to run serverless cron jobs beside REST API endpoints, and how background jobs should share logs with the same functions.
If you inline slow webhook work before 2xx, providers retry and you duplicate side effects; if you hide cron on a single host, failures go quiet; if you mix end-user auth and machine-to-machine auth on one surface, debugging and observability get worse.
This hub maps those questions to Inquir primitives—API gateway routes, scheduled pipelines, async jobs—so serverless cron jobs, webhook processors, and public JSON APIs do not each invent a separate deploy story.
Where playbooks gap
Why migration playbooks often fail
Patterns from other platforms often carry hidden assumptions about IAM, runtime limits, or workflow primitives that do not map one-to-one.
A safer rollout is incremental: implement one use case end-to-end, confirm auth, retries, and observability, then expand to the next workload.
How Inquir helps
One platform for HTTP APIs, serverless cron jobs, webhooks, and background jobs
Run serverless AI agents, webhook processors, background jobs, and REST API endpoints on one gateway with isolated containers and shared observability. Functions on Node 22, Python 3.12, and Go 1.22 follow a Lambda-style event model when you need parity with existing handlers. The same function can answer HTTP tool routes, run inside scheduled pipelines for serverless cron jobs, and continue as async jobs—one logging and isolation story instead of three deploy paths.
Layers and optional warm containers help whether traffic is a burst of model tool calls or steady serverless cron jobs. Tune cost and latency against real traffic, not guesses.
Shipping REST API endpoints or webhook processors? See serverless API Gateway for auth, rate limits, and routes.
Orchestrating background jobs or serverless cron jobs? Explore serverless pipelines for schedules, retries, and DAG steps.
Running serverless AI agents on Node, Python, or Go? See serverless functions and runtimes for handler conventions.
What you get
Use cases by workload type
Serverless AI agents
Give each tool its own function behind the gateway, lock routes down with API keys, store provider tokens as per-function environment variables, and lean on warm pools when the model hammers tools in a tight loop.
Webhook processors
Workload: provider callbacks. Failure mode: timeouts and duplicate deliveries. Why Inquir: verify quickly on HTTP routes, return fast, continue heavy work via jobs or pipelines.
REST API endpoints
Workload: public API surfaces. Failure mode: routing monolith growth. Why Inquir: split route groups that change together while keeping ingress controls centralized.
Serverless cron jobs & scheduled pipelines
Run serverless cron jobs as scheduled pipelines: cron validated at save time, run history next to HTTP executions, retries you can reason about, and shared secrets/logs with the same functions that power REST API endpoints and webhook processors.
Background jobs
Workload: async continuation after request acknowledgement. Failure mode: long request paths and duplicate side effects. Why Inquir: queue work with shared observability and retry controls.
LLM pipelines
Workload: staged AI flows. Failure mode: expensive all-or-nothing retries. Why Inquir: break retrieval, moderation, tool calls, and summarization into observable stages.
What to do next
A practical way to use this hub
Use one guide to ship one real path first; then scale the architecture based on evidence.
Pick one entry point
Start with the user or system trigger you already have: HTTP route, webhook, schedule, or async job.
Ship a minimum reliable version
Implement validation, auth, and clear response contracts before adding orchestration.
Add retries and fan-out deliberately
Only introduce pipelines, queues, or warm pools where logs show bottlenecks.
When it fits
When this hub is useful
When this works
- You need to choose and implement one backend pattern quickly without guessing platform behavior.
- You want architecture guidance tied to the real Inquir model: workspace, gateway, functions, pipelines, and jobs.
When to skip it
- You need legal/compliance commitments. Treat these pages as technical implementation guidance, then run formal review.
FAQ
FAQ
Do I need separate products for schedules and APIs?
No. One deployment gives you gateway routes and pipeline triggers, schedules included. The guides split topics so each page stays readable.
Where do I start if I am new?
Create a workspace from the home page, deploy a template such as the AI summarizer, hit the gateway URL once, then open the guide that matches your next integration.
How do guides relate to docs.inquir.org?
Guides tell the story; documentation lists limits, CLI flags, and configuration fields you will rely on in production.