Agentic Workflows: Designing Multi-Step AI Pipelines That Actually Work

An agentic workflow is a sequence of LLM calls, tool invocations, and decisions that together accomplish a task no single prompt could handle. The challenge isn't writing the steps — it's making the workflow reliable, observable, and safe when things go wrong.

Four core workflow patterns

1. Sequential Pipeline

Step A → Step B → Step C. Output of each step is input to the next. Simplest pattern, easiest to debug. Use when steps have strict dependencies.

Use for: Document processing, report generation, multi-stage analysis

2. Parallel Fan-Out + Merge

Multiple steps run concurrently (e.g., research 5 competitors simultaneously), then results are merged. Dramatically reduces total latency for independent subtasks.

Use for: Market research, multi-source data gathering, bulk classification

3. Plan-Then-Execute

First LLM call produces a structured plan (a list of steps). Subsequent calls execute each step in order. Handles tasks where the exact steps aren't known upfront.

Use for: Complex research tasks, adaptive workflows, code generation pipelines

4. Human-in-the-Loop (HITL)

Agent pauses at designated checkpoints, presents output for human review, and only continues after approval. Critical for high-stakes workflows where errors are costly.

Use for: Contract generation, financial approvals, customer-facing content

Parallel workflow with HITL on MoltBot

from moltbot.workflow import Workflow, parallel, checkpoint

wf = Workflow("competitor-analysis")

@wf.step
async def gather_data(company_list):
    return await parallel([
        research(c) for c in company_list  # concurrent requests
    ])

@wf.step
async def synthesize(results):
    return await agent.run("Synthesize these findings...", context=results)

@wf.step
@checkpoint(require_approval=True)   # human reviews before continuing
async def generate_report(synthesis):
    return await agent.run("Generate final board-ready report", context=synthesis)
      

Error recovery patterns

Retry with backoff: Transient failures (timeouts, rate limits) should auto-retry with exponential backoff. Max 3 retries before escalating.
Step-level checkpointing: Save state after each successful step. On failure, resume from the last checkpoint — don't restart the whole workflow.
Fallback steps: If an LLM call fails, fall back to a simpler model or a cached result rather than failing the whole workflow.
Dead letter queue: Failed workflows that can't recover should be routed to a review queue with full state for manual intervention.

Visual workflow builder on MoltBot

Drag-and-drop pipeline builder, parallel execution, HITL checkpoints, step-level retry, and full observability. 14-day free trial.

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