How AI Architects Design Agentic Workflows
AI architects design agentic workflows by selecting one of three proven patterns: the router, which classifies incoming requests and delegates them to the right specialist; the planner-executor, which decomposes a complex goal into a dynamic sequence of subtasks; and the crew, which runs specialised agents in parallel on independent workstreams. The right choice depends on the task structure — not on which framework is trending this quarter.
What Is an Agentic Workflow?
An agentic workflow is any AI system where an LLM makes decisions that determine the next step in a process, rather than following a fixed script. Unlike a static prompt-response chain, an agentic workflow loops, branches, calls tools, and sometimes spins up other agents. The system adapts at runtime.
The distinction matters because most businesses talk about wanting “AI agents” when what they actually need is a well-designed agentic workflow. Buying a framework like LangGraph or CrewAI does not give you a working system. Someone has to decide the pattern first — and that is the AI architect’s job.
What Are the Three Core Agentic Workflow Patterns?
Every agentic system in production today maps onto one of these three patterns, or a deliberate combination of them. Understanding the shape of each prevents the most common mistake: choosing a complex multi-agent crew when a simple router would solve the problem in a third of the time.
| Pattern | How it works | Best for | Typical frameworks |
|---|---|---|---|
| Router | A central LLM classifies the incoming request and routes it to a specialist agent or function | Well-defined task types, customer-facing systems, cost optimisation | LangGraph, custom classifier chains |
| Planner-Executor | A planner LLM writes a step-by-step plan; an executor carries out each step in sequence, with the planner revising as needed | Open-ended research, multi-step automation, tasks with unknown sub-steps upfront | LangGraph plan-and-execute, custom agent loops |
| Crew | A group of specialised agents work in parallel on independent sub-tasks, with results merged by an orchestrator | Tasks that decompose into independent workstreams, speed-critical pipelines | CrewAI, AutoGen, multi-agent graphs |
When Should You Use the Router Pattern?
The router pattern is the right choice when incoming requests fall into a known set of categories, and each category has a distinct best handler.
A router LLM sits at the front of the system, reads the incoming task, and delegates: a billing question goes to a billing agent; a technical fault goes to a debug agent; a simple lookup hits a fast, cheap model while a complex reasoning task escalates to a more capable one. The router never executes — it only classifies and hands off.
This is the most commonly deployed pattern in production for a reason: it is efficient. Cost optimisation is a genuine advantage: easy queries run on small, fast models, and expensive reasoning capacity is reserved for tasks that genuinely need it. Customer support platforms, internal helpdesks, and document-routing systems are natural homes for the router pattern.
The critical caveat: the router is a single point of failure. If the classification step misfires, the whole system misfires. An AI architect designing a router-based system must build in fallback handling, confidence thresholds, and logging at the routing layer. The router’s accuracy sets the ceiling for the entire system, which means evaluation of the routing step is not optional — it is the first thing you instrument.
When Does the Planner-Executor Pattern Make Sense?
The planner-executor pattern applies when the sequence of steps required to complete a task cannot be known in advance.
A planner LLM receives a high-level goal — “research the competitive landscape for this product and produce a structured brief” — and generates a plan: search for competitors, extract key metrics, compare pricing structures, identify positioning gaps, draft the output. An executor LLM (or a set of executors) then works through each step. Crucially, the planner can revise the plan mid-execution: if step three returns unexpected data, the plan adjusts before step four runs.
This separation of thinking and doing is one of the most important principles in agentic architecture. Asking a single model to plan, execute, evaluate, and revise simultaneously overloads its context and degrades quality at every stage. Splitting those responsibilities produces more reliable systems and makes failures easier to diagnose: you can see exactly where the plan diverged from reality.
The tradeoff is latency. A planning phase always adds time before any execution begins, and the planner-executor loop can run multiple rounds before producing a final output. For real-time or conversational use cases, this is often disqualifying. For asynchronous or back-office automation, the quality gain is usually worth the wait. A well-designed planner-executor system uses a capable model for the planning step and a faster, cheaper model for individual execution steps — separating the cost of strategy from the cost of action.
What Is the Crew Pattern and When Do You Use It?
The crew pattern treats a complex task like a construction project: break it into independent workstreams, assign a specialist to each, run them in parallel, then have an orchestrator assemble the outputs.
A content operation might run a research agent, a drafting agent, a fact-checking agent, and a formatting agent simultaneously, with results merged at the end. A financial analysis system might assign separate agents to pull market data, analyse company filings, and summarise analyst commentary in parallel, combining everything into one coherent report.
The crew pattern delivers speed through parallelism and quality through specialisation. Focused agents with narrow scopes outperform generalist agents handling everything at once — their context windows stay clean, their prompts stay sharp, and their failure modes stay contained.
The crew pattern also requires the most architectural discipline to implement correctly. An AI architect must define clean interfaces between agents: what does each agent receive? What exactly does it return? How does the orchestrator handle a partial failure — if one crew member fails, does the whole workflow stop, or does the orchestrator proceed with degraded output? In frameworks like CrewAI, role definitions and task delegation are first-class concepts. In LangGraph, you build the same structure explicitly as a state graph. Neither is inherently better; the choice depends on how much fine-grained control the system requires.
How Does an AI Architect Choose Between Patterns?
The pattern decision should be driven by the task structure, not by tool preference. This is where the diagnose-first principle matters most: no one should be writing agent code before the task has been mapped.
An AI architect works through three questions before touching any framework:
- Is the task type known at entry? If yes, a router is probably sufficient. If the incoming request could be anything, a planner is needed to figure out what to do next.
- Are the sub-tasks sequential and interdependent? If the output of step three changes what step four does, you need a planner in the loop to revise the plan at runtime. A fixed pipeline will not handle this.
- Can the task decompose into genuinely independent workstreams? If yes, a crew delivers speed. If sub-tasks depend on each other, the crew pattern creates coordination overhead that destroys its own advantage.
Most real systems combine patterns. A router at the front classifies incoming requests; complex requests pass to a planner-executor system; that system spins up a crew for the steps that can be parallelised. The AI architect’s job is to make this composition explicit and governable — to document which pattern handles which layer and why — not to make it clever.
The most expensive mistake in agentic workflow design is choosing complexity as a default. A router that correctly classifies requests handles the majority of business use cases without a planner or a crew. The planner and the crew earn their overhead when the task genuinely needs them. An AI architect who reaches for a multi-agent crew to handle a three-category routing problem has not diagnosed the business. They have imported a pattern from a tutorial and bolted it onto a workflow that did not require it.
Frequently Asked Questions
What is the difference between the router and the planner-executor pattern?
A router selects which agent handles a task. A planner-executor generates a multi-step sequence of actions and revises that sequence as execution proceeds. Use a router when the task type is known at entry; use a planner-executor when the steps required are unknown until the work begins.
Can you combine agentic workflow patterns?
Yes — and most mature production systems do. A common architecture uses a router at the entry point to handle well-understood task types, with a planner-executor backing it for open-ended requests, and a crew pattern for the sub-tasks that can be parallelised. The AI architect’s role is to make the composition deliberate and testable rather than organic and opaque.
Which frameworks implement these patterns?
LangGraph implements all three through explicit state graphs and conditional routing. CrewAI is optimised for crew-style role-based collaboration. AutoGen supports multi-agent conversation and task delegation. The framework choice should follow the pattern decision, not precede it.
How do you test an agentic workflow before deploying it?
Test each layer independently: route classification accuracy, planner output quality on representative inputs, executor tool-call reliability, and crew agent interface contracts. An AI architect defines evaluation criteria for each layer before deployment, not after the system is live.
What is the biggest mistake when designing agentic workflows?
Choosing a pattern before diagnosing the task. Teams reaching for multi-agent crews to handle what is effectively a classification problem add latency, cost, and failure modes with no corresponding benefit. Map the task structure first; the pattern selection follows from that map.
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