Agentic AI Architecture Patterns

What it is

There is no single way to build an agentic system. The same goal — “investigate why the dashboard is wrong” — can be solved by a single agent with five tools, by an orchestrator dispatching to specialist sub-agents, by a graph-based state machine, or by a workflow with deterministic steps and one LLM call per node.

Each of these is a pattern. The patterns differ in cost, latency, predictability, debuggability, and capability ceiling. Choosing the right one is the most important architecture call in agentic system design — more important than the choice of model, framework, or vendor.

Why it matters

The same business problem solved with the wrong pattern fails for predictable reasons.

Use a single-agent loop for a multi-domain coordination problem and you get long contexts, expensive runs, and confused decisions. Use a multi-agent orchestrator for a simple linear task and you pay 5× the latency and 10× the cost for no benefit. Use a deterministic workflow for a problem that genuinely requires runtime decisions and you ship a brittle pipeline that breaks on edge cases.

The patterns are not interchangeable. Knowing which fits which problem is what separates senior agentic architects from people who pick the framework first.

The patterns

The five most useful patterns in 2026, in roughly increasing complexity.

1. Prompt chaining (deterministic workflow with LLMs). A pipeline where each step is an LLM call with a fixed input/output shape. No loop, no autonomy. Step 1 extracts entities. Step 2 classifies them. Step 3 generates the output. Use when the structure is known and stable. The simplest, cheapest, most predictable. Wrong choice when you need runtime decisions about what to do next.

2. Routing. An LLM classifies the input and routes to one of several specialized handlers. “Is this a billing question, a tech question, or general?” → dispatch to the right downstream prompt. Cheap, useful, often the right answer for support and intake.

3. Single-agent loop with tools. One LLM in a loop with a tool set. The model decides what tool to call next based on the goal and accumulated context. Right pattern for tasks where the path is variable but the work fits in one model’s context — research, troubleshooting, code review.

4. Orchestrator-worker (multi-agent). A coordinator agent decomposes the task and dispatches to specialist agents. Each specialist has its own tool set and prompt. The orchestrator integrates the results. Right pattern when sub-tasks are large enough to merit dedicated context, when specialists have very different tool sets, or when parallelism matters. Higher cost, higher coordination complexity.

5. Graph-based state machine. The agent’s behavior is encoded as a graph of states and transitions. LLM nodes make decisions; deterministic nodes handle structure. LangGraph and Temporal-style workflows live here. Right pattern when the system is genuinely production — observability, deterministic recovery, durable state, parallel branches all become first-class.

Choosing the pattern

A practical decision tree:

• Is the task structure fixed? → Prompt chaining.
• Is the task to dispatch incoming requests? → Routing.
• Does the task fit in one model’s context, with variable steps? → Single-agent loop.
• Are sub-tasks large or genuinely parallel? → Orchestrator-worker.
• Is the system production-critical with durability and observability needs? → Graph-based state machine.

The trap is starting with the most complex pattern. Most production systems should start with prompt chaining or single-agent loops, and graduate to orchestrator-worker or graph-based patterns only when the simpler version proves insufficient.

Vendor comparison

The frameworks each lean toward different patterns.

Framework	Native pattern strength	Notes
LangGraph	Graph-based state machine	The strongest production option for graph patterns; explicit state, durability, replay
CrewAI	Orchestrator-worker (role-based)	Easy to start with multi-agent; less mature for production scale
AutoGen	Multi-agent conversation	Strong research framing, good patterns library
OpenAI Agents SDK	Single-agent + handoffs	Clean API for single-agent loops with delegation
Anthropic / MCP-native	Single-agent + tools via MCP	Strong for tool-rich single agents
Temporal + LLM	Graph-based, durable workflows	Borrowed from application workflow engines; excellent for long-running

For new builds in 2026, my default recommendation is to start with the simplest pattern that fits and let evidence promote you to a more complex one. LangGraph when state machines and durability genuinely matter. OpenAI Agents SDK or Anthropic’s MCP-based agents for cleaner single-agent work tied to one provider. CrewAI / AutoGen for fast prototyping of multi-agent ideas before committing to production.

Yoann’s take

My architectural rule: pick the simplest pattern that solves the problem, and let production pressure promote you to a more complex one. Most teams start with multi-agent orchestration because it sounds modern and end up with three agents arguing in expensive context windows about a problem one agent could have solved. The right progression: prompt chain → routing → single-agent loop → orchestrator-worker → graph state machine, with each promotion driven by evidence that the previous pattern is insufficient. The other rule: deterministic where you can, agentic where you must. Inside any agentic system, the parts that have known structure should not be LLM calls — they should be Python functions. The LLM’s job is variability, not arithmetic.

— Yoann

Related reading

• What Is an Agent, Really
• Multi-Agent Orchestration
• Production Patterns for Agentic Systems
• Tool Use and Function Calling

External references

• Anthropic — “Building effective agents”
• LangGraph documentation and pattern library
• “Agentic Design Patterns” — practitioner essays