Agentic workflow backend for multi-step AI agents
Agentic workflows need more than tool endpoints: durable orchestration across planning, tool calls, validation, human approval, and cleanup—with retries per step, traces per model turn, and secrets kept off the model path. Inquir maps each step to serverless functions and pipelines behind one gateway.
Last updated: 2026-06-23
Answer first
Direct answer
Agentic workflow backend for multi-step AI agents. Each workflow step is a serverless function: plan, call-tool, validate, notify, approve. Pipelines connect steps with retries, branching, and durable state—so a failed enrichment step retries without re-running the model planning step.
When it fits
- Multi-step agents that call tools, validate results, and notify humans
- Workflows that need durable state, per-step retries, or approval gates
Tradeoffs
- In-memory state machines lose progress on restart. There is no durable checkpoint between tool calls, no audit trail for compliance, and no way to pause for human approval without custom plumbing.
- Calling tools directly from the orchestrator couples model traffic with database credentials and third-party API keys in the same runtime—harder to scope permissions and rotate secrets.
Workload and what breaks
Why agentic workflows need a dedicated backend
A single LLM loop with inline tool calls works in demos. Production agentic workflows span minutes or hours: research, act, verify, escalate, notify. When step four fails, you need to retry only that step—not replay the entire conversation from scratch.
Without a real backend, orchestration logic, tool execution, and side effects share one process. One bad deploy or memory leak takes down every agent workflow simultaneously.
Trade-offs
Where lightweight agent orchestration breaks
In-memory state machines lose progress on restart. There is no durable checkpoint between tool calls, no audit trail for compliance, and no way to pause for human approval without custom plumbing.
Calling tools directly from the orchestrator couples model traffic with database credentials and third-party API keys in the same runtime—harder to scope permissions and rotate secrets.
How Inquir helps
Serverless functions + pipelines as the agentic layer
Each workflow step is a serverless function: plan, call-tool, validate, notify, approve. Pipelines connect steps with retries, branching, and durable state—so a failed enrichment step retries without re-running the model planning step.
Tool calls go through authenticated gateway routes with per-tool secrets. The orchestrator sees structured JSON; the platform handles isolation, warm pools for hot tools, and execution history per step.
What you get
Agentic workflow backend patterns
Durable multi-step runs
Pipeline steps persist state between model turns. Resume after failure without replaying completed steps.
Tool calls as gateway functions
Each tool is an authenticated HTTP endpoint with isolated containers and scoped secrets—not inline code in the orchestrator.
Human-in-the-loop gates
Pause the pipeline before sensitive actions; continue when approval arrives via webhook or manual trigger.
Branching and fan-out
Route to different steps based on tool output, confidence scores, or policy checks—without nested if/else in one monolith.
What to do next
How to build an agentic workflow backend on Inquir
Separate orchestration from execution: the model plans, gateway functions run tools, pipelines carry durable work with retries.
Define workflow steps
Map each agent action to a function or pipeline step: plan, tool-call, validate, notify, approve.
Wire tools to gateway routes
Deploy one function per tool with API key auth and workspace secrets. Return structured JSON the orchestrator passes back to the model.
Connect steps in a pipeline
Use pipelines for durable runs: retries per step, branching on tool output, human approval waits, and full execution history.
Code example
Agentic workflow: plan → tool → validate → notify
The orchestrator calls gateway tools synchronously for fast steps; slow or retry-heavy work continues in a pipeline.
export async function handler(event) { const { query, runId } = JSON.parse(event.body || '{}'); if (!query) return { statusCode: 400, body: JSON.stringify({ error: 'query required' }) }; const { instanceId } = await global.durable.startNew('research-agent', undefined, { query, runId }); return { statusCode: 202, body: JSON.stringify({ runId: instanceId, status: 'started' }) }; }
export async function handler(event) { const { query } = JSON.parse(event.body || '{}'); const results = await searchInternalDocs(query); return { statusCode: 200, body: JSON.stringify({ results, count: results.length }) }; }
When it fits
Best fit for agentic workflow backends
When this works
- Multi-step agents that call tools, validate results, and notify humans
- Workflows that need durable state, per-step retries, or approval gates
When to skip it
- Single-turn chat with one tool call and no side effects
FAQ
FAQ
How is this different from tool calling backend?
Tool calling covers individual HTTP endpoints per tool. Agentic workflow backend adds durable orchestration: multi-step pipelines, branching, human approval, and checkpointed state across model turns.
Can I use LangGraph or CrewAI with Inquir?
Yes. Keep your orchestration framework; point tool calls at Inquir gateway routes and offload durable steps to pipelines via global.durable.startNew().
What happens when a pipeline step fails?
The step retries according to pipeline policy. Completed steps are not re-run. Execution history shows which step failed, how many retries occurred, and the error output.