Agentic Design Patterns: The Complete Guide to Building Reliable AI Agents

Gartner predicts that over 40% of agentic AI projects will be scrapped by the end of 2027, undone by rising costs, unclear value, and weak controls, according to its June 2025 forecast. The teams that survive that cull will not be the ones with the best model. They will be the ones who stopped hand-wiring agents like flowcharts and started building on proven agentic design patterns.

This guide is the field manual: what each pattern is, the problem it solves, when to reach for it, and how to compose patterns into systems that hold up in production instead of breaking in a demo.

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What Are Agentic Design Patterns?

Agentic design patterns, also called AI agent design patterns, are reusable architectural solutions to the recurring problems you hit when you build autonomous systems on top of large language models. Think of them as the Gang of Four design patterns, but for AI agents: a shared vocabulary for structuring how an agent reasons, acts, checks its own work, and coordinates with other agents.

They are architecture patterns, not UX frameworks and not prompt tricks. A pattern names a structural decision (does the agent critique its own output? does it plan before acting? does it hand work to specialists?) so that decision becomes something you can test, observe, and reuse rather than an accident buried in a prompt.

The mistake teams make is rarely the model they picked. It is building agents as brittle, hand-wired scripts with no pattern discipline, then wondering why they fall apart the first time reality deviates from the happy path.

Before the catalog, one distinction sits underneath everything.

Workflows vs. Agents: The Foundational Distinction

Anthropic, in its widely cited guide Building Effective Agents, draws the line every builder should internalize. Workflows orchestrate LLMs and tools along predefined code paths. Agents let the model dynamically direct its own process and tool use, deciding at runtime how to reach the goal.

Neither is better. Workflows give you predictability for well-defined tasks. Agents give you flexibility when you cannot hardcode the path in advance. The best agentic workflows mix both: a workflow scaffolds the reliable parts, and an agent takes over where judgment is needed. If you want the deeper split between the two mindsets, our breakdown of agentic AI vs. AI agents goes further.

Why Patterns Matter (and Why Flowcharts Fail)

Here is where the popular no-code tools drop out. Zapier, Make, and n8n hard-code every branch at design time. If this, then that, forever. That is not agency, it is a static diagram with an LLM bolted on. A flowchart cannot reflect on a bad result, re-plan when the situation changes, or recover from an error it did not anticipate.

Patterns are what separate a durable agent from a chatbot wrapped in a while-loop. Naming the pattern (Reflection, Planning, Tool Use, Orchestrator-Workers) is exactly what makes the system auditable and improvable instead of a black box you pray over.

The Core Agentic Design Patterns

Andrew Ng's team at DeepLearning.AI popularized the canonical taxonomy in Agentic Design Patterns Part 1, and it remains the backbone most practitioners organize around. In their analysis, wrapping GPT-3.5 in an agentic workflow pushed its HumanEval coding score from 48% up to as high as 95%, a bigger jump than upgrading the base model. The patterns, not the raw model, did the heavy lifting.

Here are the four core patterns at a glance.

Reflection

The agent examines its own work and improves it. Instead of accepting the first draft, a second pass (or a dedicated evaluator step) spots weak arguments, bugs, or missing information and revises. This is the single cheapest quality upgrade available, because it turns a one-shot guess into an iterative loop. Use it for code, analysis, and any output where correctness beats speed.

Tool Use

The agent is given tools: web search, code execution, a database query, an email send, a Stripe refund. Tool Use is what moves an agent from talking about work to doing work. The hard part is not the tool, it is the interface. Anthropic found teams spend more time designing clean tool definitions than tuning the core prompt, because a poorly described tool is a reliably misused one.

Planning

The agent produces a multi-step plan, then executes it. Outline first, then research, then draft, then check. Planning shines when the number and order of steps cannot be known upfront. It also pairs naturally with Reflection: plan, act, evaluate, re-plan. For how planning fits into a full system layout, see our guide to AI agent architecture.

Multi-Agent Collaboration

More than one agent works together, each specialized, splitting tasks and combining results. A researcher agent gathers, a writer drafts, a reviewer checks. Multi-agent setups unlock tasks too big for one context window or one skillset, but they add coordination cost, so do not reach for them until a single agent genuinely cannot cope. The coordination itself is its own discipline, covered in our deep dive on AI agent orchestration.

ReAct: Reason then Act

ReAct interleaves reasoning traces with actions, so the agent thinks, acts, observes the result, then thinks again. It is the workhorse loop behind most tool-using agents and a good default when a task needs both reasoning and external calls. We break down the loop in detail in our post on ReAct agents.

July 26, 2026

AI Agent Orchestration: How to Coordinate Multi-Agent Systems

AI agent orchestration coordinates multiple reasoning agents toward one goal. Learn the four patterns, the architecture, and how to run a multi-agent system with no code.

Agentic Workflow Patterns

Where the core patterns describe how a single agent behaves, workflow patterns describe how you wire LLM calls together into a reliable pipeline. Anthropic's five are the ones to know, and they are the building blocks most production systems actually run on.

Building Effective AI AgentsDiscover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI.anthropic.com

A quick mental model of orchestrator-workers, the pattern behind most serious multi-file coding and research agents:

                 ┌────────────────┐
   task ───────▶ │  Orchestrator  │ ──── plans & delegates
                 └────────┬───────┘
             ┌────────────┼────────────┐
             ▼            ▼            ▼
        ┌─────────┐  ┌─────────┐  ┌─────────┐
        │ Worker  │  │ Worker  │  │ Worker  │
        └────┬────┘  └────┬────┘  └────┬────┘
             └────────────┼────────────┘
                          ▼
                 ┌────────────────┐
                 │  Orchestrator  │ ──── synthesizes result
                 └────────────────┘

The difference from plain parallelization is flexibility. In parallelization you know the subtasks in advance. In orchestrator-workers the lead agent decides them at runtime based on the input, which is exactly why it handles open-ended work that a fixed flowchart cannot.

Supporting Patterns You Will Compose In

These patterns rarely stand alone. You bolt them onto the core loop as the task demands, and each already has a dedicated home on this blog.

• Retrieval / Agentic RAG: the agent decides what to look up and when, rather than stuffing a fixed context. See agentic RAG.
• Memory: the agent retains what matters across runs and lets the rest decay. See AI agent memory.
• Human-in-the-loop: the agent pauses for approval before high-stakes actions, then resumes. See human-in-the-loop AI agents.
• Guardrails and error recovery: validation, fallbacks, and retries that keep a bad step from becoming a bad outcome.

That last one is where the demo-to-production gap opens up, and it is worth its own section.

How to Choose the Right Pattern

Most guides list patterns. Few help you pick. Here is a decision framework you can actually use. Match your task's dominant constraint to a starting pattern, then layer from there.

Two rules keep you out of trouble:

1. Start simple, add complexity only when it earns its place. A single agent with Tool Use and Reflection beats a five-agent swarm for most jobs. Anthropic's own advice is to find the simplest solution first and increase complexity only when it demonstrably improves outcomes.
2. Patterns stack. A production agent is often Planning plus Tool Use plus Reflection plus a human-in-the-loop gate, not one pattern in isolation.

And the anti-patterns to avoid:

• Reaching for multi-agent when one agent with good tools would do
• Shipping any agent with no reflection or evaluation loop
• Letting an agent take irreversible actions with no human gate
• Treating a fixed flowchart as if it were an agent

From Patterns to Production: Where Durability Begins

Here is the uncomfortable truth the pattern diagrams leave out. A pattern is an architecture, not a runtime. Reflection loops, orchestrator-workers, and human-in-the-loop gates all assume something is faithfully executing them: persisting state between steps, retrying failed tool calls, keeping a run alive for hours, recording every action so you can see what happened.

Flowchart tools give you the shape of a pattern with none of these guarantees. DIY framework scripts give you the guarantees only if you build them yourself, which means you are now maintaining a distributed-systems project instead of shipping agents. This is the gap where those 40% of projects die: the pattern was fine, the execution around it was not.

Durable, observable execution is the missing half. That is the bridge to how Rerun fits.

Implementing Agentic Design Patterns with Rerun

Rerun is the platform that runs the patterns above with the durability they assume and the transparency they lack. You design with Reflection, Planning, Tool Use, and Orchestration; Rerun runs them on always-on machines with state, retries, human approvals, and a live dashboard, so a pattern survives real traffic instead of silently failing.

It is not a chatbot, because it does the work instead of talking about it. It is not Zapier, Make, or n8n, because there are no fixed flowcharts to wire. It is not a DIY framework you babysit on a VPS. It is the runtime that makes a pattern durable and, crucially, one you can watch.

The human-in-the-loop pattern is a good example of the difference. In Rerun, an agent chasing overdue invoices stops before it emails a client, asks you how to proceed, and resumes exactly where it paused once you approve from the app or Slack. That is a governance pattern with a real runtime behind it, not a checkbox in a diagram.

Want to see the exact brief that turns a pattern into a running agent? Here is a compact orchestrator-workers configuration.

{
  "goal": "Produce a weekly competitor intelligence report",
  "pattern": "orchestrator-workers + reflection",
  "orchestrator": "Break the goal into research subtasks based on this week's inputs, delegate to workers, then synthesize a single report",
  "workers": [
    "News scanner: gather notable competitor moves from the last 7 days",
    "Pricing watcher: check each competitor's pricing page for changes",
    "Sentiment reader: summarize recent user discussion"
  ],
  "reflection": "Before sending, critique the draft for unsupported claims and missing sources, then revise",
  "human_in_the_loop": "Pause for approval before publishing to the shared channel",
  "schedule": "Every Monday at 07:00"
}

The Frameworks Fit Underneath the Patterns

A common question: where do LangGraph, CrewAI, and AutoGen sit in all this? They are implementations of these patterns, not alternatives to them. A framework gives you primitives for wiring Planning or Multi-Agent loops in code. It still leaves the runtime, the observability, and the human gates for you to solve. If you are weighing them, our rundown of AI agent frameworks compares the main options, and the recurring lesson is the same: the framework helps you express a pattern, it does not run it in production for you.

The Bottom Line

Agentic design patterns are the vocabulary that turns fragile demos into durable systems. Learn the core four (Reflection, Tool Use, Planning, Multi-Agent), the five workflow patterns that compose them, and the supporting patterns you bolt on as the task demands. Then choose by constraint, start simple, and stack from there.

But a pattern is only as good as the runtime executing it. The teams whose agents make it past 2027 will be the ones who paired the right pattern with durable, observable execution instead of a flowchart or a fragile script. That pairing is exactly what Rerun exists to give you.

Stop drawing agents. Start watching them run. Try Rerun free for 7 days and see a pattern work live.