Top 10 agentic design patterns you should know
There's a particular flavor of chaos that happens when companies try to "add AI" to their operations without understanding how autonomous systems actually work. They bolt a chatbot onto an existing process, watch it confidently hallucinate its way through customer inquiries, and then wonder why adoption stalled somewhere around week two.
The problem isn't the AI. It's the architecture.
Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. But here's what that stat doesn't tell you: without the right design patterns, most of those implementations will fail.
Agentic design patterns are the architectural blueprints that determine how AI agents reason, act, coordinate, and hand off to humans when judgment is required.
For product managers building anything beyond a basic Q&A bot, these patterns are non-negotiable.
Key takeaways
Agentic design patterns are reusable blueprints for autonomous systems. They define how agents think, act, collaborate, and learn, moving beyond simple prompt-response interactions into multi-step reasoning and execution.
These patterns span cognitive strategies and workflow orchestration. From reasoning frameworks like ReAct to coordination models like multi-agent collaboration, each pattern addresses a different layer of autonomous behavior.
Human-centric design separates useful AI from frustrating AI. Patterns that embed transparency, human oversight, and clear handoffs create systems users actually trust. Platforms like Moxo are built around this principle, keeping humans accountable for decisions while AI handles coordination.
Choosing the right pattern depends on your process complexity. Simple tasks need simple patterns. Multi-party workflows with approval gates and exceptions require orchestration patterns that preserve human accountability.
Agentic design patterns at a glance
1. ReAct (reason + action): Thinking before doing
The ReAct pattern alternates between reasoning steps and action execution. Instead of jumping straight to an answer, the agent thinks through its approach, takes an action, observes the result, and reasons again.
For product managers, ReAct matters because it creates explainability. When an agent shows its reasoning chain, users can follow the logic. This is where Moxo's AI agents shine: they prepare and validate work with traceable steps, so humans reviewing decisions understand exactly how conclusions were reached.
2. Reflection: Self-assessment for better accuracy
Reflection patterns let agents review their own outputs before committing to them. The agent generates a response, evaluates it against criteria, identifies weaknesses, and revises.
This feedback loop dramatically reduces hallucinations. In Moxo's workflow orchestration, AI agents can flag uncertainty before routing decisions to humans, building trust through acknowledged limitations rather than false confidence.
3. Planning and sequential orchestration: Breaking down complex work
Planning patterns generate multi-step task sequences before execution begins. Sequential orchestration extends this by chaining actions in a linear pipeline where each step's output feeds the next.
For external-facing products, planning enables progressive disclosure. Users see what's coming next and understand where they are in a process. Moxo's visual workflow builder makes these multi-step sequences explicit, so clients and internal teams always know what happens next.
4. Tool use: Leveraging external capabilities
Tool use patterns let agents call external functions, APIs, databases, and services. This transforms AI from a text generator into an execution engine that can actually do things.
Without tool use, agents can only talk about work. With it, they can check inventory, update records, and trigger workflows. Moxo's integration capabilities embed these tool-use patterns directly into business processes, connecting AI agents to the systems where real work happens.
5. Prompt chaining: Stepwise problem solving
Prompt chaining splits complex tasks into a series of focused prompts, each building on the previous response. This maintains context across multi-stage interactions.
The pattern helps agents handle nuanced, evolving requests. In Moxo, this translates to AI agents that maintain context across an entire client journey, not just isolated interactions.
6. Multi-agent collaboration: Teams of specialist agents
Multi-agent collaboration treats multiple AI agents as a coordinated team. Each agent specializes in a role (data retrieval, analysis, compliance checking) and they work together on tasks too complex for any single agent.
Multi-agent collaboration mirrors team dynamics, letting products offer coordinated responses that reflect different expertise domains.
This pattern enables products to offer multi-domain responses without building a single impossibly complex agent. Moxo's process orchestration coordinates these specialist capabilities across departments and external stakeholders.
7. Orchestration patterns: Coordinating autonomous workflows
Orchestration patterns control how multiple agents cooperate, from sequential pipelines to concurrent execution. Without orchestration, multi-agent systems produce disjointed outputs.
AI doesn't replace decisions. It replaces the work required to get to them.
For products serving external clients, orchestration is essential. One G2 reviewer noted: "Moxo has been a game-changer for our team's onboarding process. Before we implemented it, we had to rely on a number of manual steps and scattered tools to get new partners onboarded, which was both time-consuming and prone to bottlenecks."
8. Human-in-the-loop: Balancing automation and control
Human-in-the-loop (HITL) patterns embed human oversight at critical decision points. The agent handles preparation, validation, and routing, but humans make the calls that matter.
Orchestration fails when humans are removed. It works when they're supported.
This is the foundation of Moxo's Human + AI model: AI agents handle twenty steps of coordination. Humans handle the two or three judgment calls that require expertise and accountability.
9. Transparency and reasoning patterns: UX for trust
As agentic systems become more autonomous, users need clear visibility into agent reasoning. Transparency patterns expose reasoning chains, confidence levels, and decision rationale.
This isn't optional. Users (and regulators) are more likely to adopt AI systems that show why decisions were made. Moxo provides operational visibility into where work stands, what's blocked, and what's moving, so teams can intervene before problems escalate.
10. Safety and guardrail patterns: Responsible autonomy
Safety patterns define operational boundaries: ethical constraints, escalation triggers, and rollback mechanisms. They ensure agents don't exceed intended operational scope.
Another G2 reviewer shared: "Moxo has helped us completely streamline our project management and client communication process. It's made our workflows much more organized, our team more accountable, and our clients more informed and confident in our process."
Why these patterns matter for process orchestration
Here's the uncomfortable truth about most AI implementations: they automate tasks without addressing coordination.
Your AI can generate perfect responses, but if those responses don't connect to your actual workflows, if there's no clear handoff to humans, no visibility into what happened, you've just built a sophisticated dead end.
Here's what this looks like with Moxo. A deal exception needs review. An AI agent monitors for the trigger, gathers context, prepares the approval request with relevant history, and routes it to the right decision-maker. Finance reviews margin impact.
Legal reviews non-standard terms. The ops lead makes the final call. The deal moves forward without the "just checking in" emails, without context that lives only in someone's inbox.
The outcome: faster execution, clearer ownership, and the ability to scale operations without adding proportional headcount.
Working with humans in the loop
Agentic design patterns aren't academic exercises. They're the difference between AI that demos well and AI that runs production operations.
For product managers, the takeaway is simple: before you build, understand the patterns. ReAct for explainable reasoning. Reflection for accuracy. Planning and orchestration for complex workflows. Human-in-the-loop for accountability. Transparency for trust. Guardrails for safety.
The goal isn't to remove humans from processes. It's to remove the manual coordination burden that prevents humans from focusing on the work that actually requires their judgment.
Ready to design agentic workflows that integrate human judgment with AI coordination? Get started with Moxo to see how process orchestration works in practice.
FAQs
What are agentic design patterns?
Agentic design patterns are structured methods and architectural blueprints for building autonomous AI agents. They define how agents reason, take action, collaborate with other agents, and hand off to humans when judgment is required.
Which pattern should I start with if I'm new to agentic design?
Start with Human-in-the-Loop (HITL). It's the foundation that ensures AI augments rather than replaces human judgment, and it forces you to think clearly about where decisions actually happen in your process.
How do I prevent agentic systems from making mistakes?
Combine reflection patterns (self-assessment), guardrail patterns (operational boundaries), and human-in-the-loop patterns (oversight at critical points). The goal is autonomy within defined constraints with human accountability at decision points.
Can these patterns work together?
Yes, they're designed to compose. A production system might use ReAct for reasoning, tool use for execution, multi-agent collaboration for complex tasks, and HITL for decisions requiring human judgment.
What makes transparency patterns different from logging?
Transparency patterns expose reasoning, not just actions. Users see why the agent made a decision, what confidence level it has, and what alternatives it considered, building trust because users can evaluate the logic.
