What is an agentic framework?

A developer on your team fires up Cursor or Claude Code, describes a goal, and watches it plan out a dozen steps, run them, and fix its own mistakes along the way. No follow-up prompt required. The infrastructure that makes this possible is called an agentic framework.

According to our 2025 AI report, 51% of Figma users working on AI products are building agents, compared to just 21% last year. Agentic frameworks are a big reason why. They’re the layer that turns a single AI response into a sustained, goal-driven workflow.

Read on to learn:

• What an agentic framework is and how the agent loop works
• Core components that make autonomous AI possible
• Common use cases across product, engineering, and design teams
• The most widely used agentic frameworks
• How Figma fits into the agentic workflow, no coding required

Agentic framework design patterns and core components

An AI agent’s autonomy doesn’t come from the model alone. It emerges from a framework that combines core components with structured design patterns. The components define what an agent can do, while the patterns define how it does it.

Think of the framework as the system that enables reasoning, memory, and action—and the design pattern as the strategy that organizes those capabilities into a working loop.

Planning, reasoning, and execution patterns

At the center of every agentic system is the ability to plan and decide what to do next. Design patterns shape how that happens. Some frameworks handle this through explicit planning stages, while others rely on chain-of-thought reasoning built into the model itself.

For example:

• In ReAct (Reason + Act), the agent alternates between thinking and acting in a loop, adjusting based on new information.
• In Plan-and-Execute, the agent builds a full plan upfront, then carries it out step by step.

Both patterns rely on the same underlying capability—reasoning—but structure it differently depending on task complexity.

Picture an agent generating design variants for a new onboarding flow. In a ReAct pattern, it explores components dynamically. In Plan-and-Execute, it maps the full flow first, then builds each screen in sequence.

You can see this same idea at work in Figma Make. Turn on Plan mode before you start a build, and Make reads your project, any attached designs, and your prompt, then puts together a structured plan in a plan.md file you can review. You can edit the plan directly or ask Make to revise it, and refine it as many times as you need before anything gets built.

Once the plan looks right, click Build, and Make executes it. Instead of jumping straight to execution, the agent pauses to lay out its approach first, the same split between deciding what to do and doing it that sits at the core of every agentic framework.

Tool integration and tool-calling patterns

Tools give agents the ability to act on external systems, not just generate text. These tools can include APIs, databases, search engines, code execution environments, or design platforms.

Tool-calling is both a capability and a pattern. The framework enables tool access, while the pattern determines when and how tools are used within the reasoning loop.

This is where the Figma MCP server comes in. It acts as a tool an agent can call to:

• Read design context, including components, variables, tokens, styles, and Code Connect mappings
• Write directly back to the canvas

Built on the Model Context Protocol (MCP), this read-and-write capability makes Figma environments accessible and actionable for agents.

Memory and context management

Memory allows agents to retain and reuse information across steps or sessions. Most frameworks separate this into:

• Short-term memory, used within a single task
• Long-term memory, retrieved across sessions (often via external systems like vector databases or Redis)

Design patterns determine how memory is used. A ReAct agent might continuously update context as it works, while a multi-agent system may share memory between specialized agents.

For example, an agent could recall your team’s token naming conventions or reuse feedback from a prior design review when generating new variants.

Orchestration and multi-agent patterns

Orchestration coordinates how tasks are executed—either by a single agent or across multiple agents.

In multi-agent patterns, a coordinator delegates work to specialized agents. For example:

• One agent reads a Figma design via MCP
• Another generates implementation-ready code
• An orchestrator sequences the workflow between them

The framework provides the routing and coordination logic, while the pattern defines how responsibilities are divided.

Evaluation and self-correction loops

Advanced agentic frameworks include evaluation mechanisms that allow agents to assess and refine their own outputs.

This capability is often embedded into design patterns as iterative loops:

• ReAct naturally incorporates feedback after each action
• Multi-agent systems may include a dedicated QA or critique agent

Agents can self-correct, but human oversight still matters. Designers and PMs still need to validate outputs before anything ships.

Popular agentic frameworks in 2026

The agentic framework landscape grew fast, then started to consolidate. Here’s a look at the frameworks product and engineering teams are most likely to run into today, from developer-built backends to the tools your team may already be working alongside.

LangChain

LangChain is one of the most widely adopted entry points into agentic development. It offers a large ecosystem, easy tool integration, and fast prototyping.

LangChain is a solid choice for teams that want to ship agentic features quickly. As complexity grows, though, it offers less control than graph-based systems.

LangGraph

LangGraph is LangChain’s graph-based sibling, where agents act as nodes connected by conditional logic.

LangGraph has become the standard many teams reach for when building production-grade agentic systems. Engineers building complex Figma-integrated workflows increasingly turn to it for that level of control.

CrewAI

CrewAI is a multi-agent framework where each agent plays a defined role within a “crew.” It’s strong for workflows that benefit from task specialization: research, synthesis, and output generation handled by separate agents working in sequence.

Microsoft Agent Framework (AutoGen + Semantic Kernel)

Microsoft’s AutoGen (built for multi-agent research) and Semantic Kernel (built for enterprise LLM integration) have now merged into a single project, Microsoft Agent Framework. Both original projects are now in maintenance mode, with new development happening under the unified framework.

Microsoft Agent Framework combines AutoGen’s group chat and reflection patterns with Semantic Kernel’s session-based state management and type safety. It’s built for teams already working in the Microsoft or Azure ecosystem, with support for the Model Context Protocol and Agent-to-Agent communication.

LlamaIndex

LlamaIndex focuses on connecting agents to structured and unstructured data. It is particularly useful for retrieval-augmented workflows, where agents need to query documents, knowledge bases, or proprietary datasets.

PydanticAI

PydanticAI is a newer, Python-native framework built around type safety and structured outputs. It suits teams that want predictable, schema-validated agent responses, which matters in design-to-code pipelines where output consistency counts.

OpenAI Agents SDK

The OpenAI Agents SDK is OpenAI’s official framework for building production-ready agents. It includes built-in support for tool use, handoffs between agents, and guardrails, and it’s seeing fast adoption among teams already building on OpenAI models.

Teams using Cursor, Claude Code, or Copilot in VS Code are likely already working with agentic patterns like these, whether or not the tool names a specific framework. The Figma MCP server works with all of them through the MCP standard.

Common use cases for agentic frameworks

Agentic frameworks now show up across nearly every function in a product org. Here’s where teams are putting them to work, well beyond engineering:

• Customer support automation: Agents handle multi-step support interactions, looking up account data, drafting responses, and escalating when needed, without a human at each step.
• Research assistants: Agents gather, synthesize, and summarize information from multiple sources. This is useful for competitive analysis, user research synthesis, or market scans.
• Coding assistants: Tools like Cursor and Claude Code use agentic frameworks to plan, write, test, and iterate on code based on a goal, not a single prompt.
• Design workflow automation: Agents apply design system tokens across flows, populate frames with realistic content, rename layers for consistency, or bring coded states back to the Figma canvas for review—all through the Figma MCP server.
• Data analysis: Agents query databases, run analyses, and generate reports on their own, which supports product analytics and business intelligence work.
• Workflow automation: Agents route tasks, validate outputs, and trigger follow-up actions across multiple tools in a business process.
• Autonomous software testing: Agents generate test cases, run them, evaluate results, and flag regressions, cutting down on manual QA hours.
• Document processing: Agents extract, classify, and route information from unstructured documents, like contracts, briefs, or research reports.

For design and product teams specifically, design workflow automation is the category worth paying closest attention to. We built Figma to support this through the MCP server and Figma Make.

What agentic frameworks mean for product design teams

Agentic frameworks are changing the handoff model. Instead of a designer producing a spec and passing it to a developer, design and code can stay in sync through shared context, mediated by agents that understand both sides.

Agents can now write directly to Figma files using real components, variables, and styles through the MCP server’s write-to-canvas capability.

The union of design systems and AI expands the designer’s workspace to include decisions that used to stay invisible. Think edge cases, loading states, error states, and all the things that showed up in code but never made it back to the canvas.

This matters for design system quality. The cleaner your components, tokens, and Code Connect mappings, the more useful an agentic framework becomes. Design systems are the context agents need to produce output that’s on-brand and consistent, not generic.

So what hasn’t changed? While agentic tools handle scale and tedium well (like bulk edits, token application, and frame population), human designers still make the judgment calls. Think of the agent as a collaborator, not a replacement.

What to keep in mind before adopting an agentic framework

Before you plug an agentic framework into your workflow, take an honest look at where your team actually stands:

• Your design system needs to be ready. Agents work best when components, variables, and styles are clean, consistently named, and, ideally, connected to code through Code Connect. Hand an agent a messy design system, and you’ll get messy output back.
• Start with bounded tasks. Bulk edits, content population, token application, and bringing coded states to the canvas are all low-risk, high-value places to start. Open-ended creative direction still benefits most from a designer’s direct involvement.
• Build review into the workflow. Agentic tools move fast, but every output needs a designer’s eye before it ships. Human-in-the-loop review isn’t optional.
• Expect the tooling to keep evolving. Capabilities like Figma’s write-to-canvas feature are new and improving quickly, so what an agent can and can't do today will likely look different in a few months.

Frequently asked questions

How is an agentic framework different from a regular AI tool?

A regular AI tool responds to one prompt at a time. An agentic framework lets an AI pursue a goal across multiple steps, using external tools, retaining memory, and self-correcting when something doesn’t work. The difference comes down to acting versus responding.

What are the most popular agentic frameworks?

The most widely used agentic frameworks include LangChain, LangGraph, CrewAI, Microsoft Agent Framework (the successor to AutoGen and Semantic Kernel), LlamaIndex, PydanticAI, and the OpenAI Agents SDK. The right choice depends on your team’s stack, workflow complexity, and whether you need single-agent or multi-agent coordination.

Do designers need to code to use agentic frameworks?

No. Tools like the Figma MCP server are built so designers can take part in agentic workflows without writing code. Designers set up the design system, define the standards, and review the agent’s output, while the agent handles execution.

Which agentic frameworks work with Figma?

The Figma MCP server works with any MCP-compatible tool, including Claude Code, Cursor, Codex, and Copilot in VS Code. These tools can read design context from Figma and write directly to the canvas using your components, variables, and styles.

What is Figma’s role in agentic AI workflows?

Figma is the design layer in an agentic workflow. Figma’s MCP server gives agents access to your components, tokens, styles, and Code Connect mappings, and lets them write designs back to the canvas. Figma Make lets designers and PMs prototype ideas without writing code. Together, they make Figma the place where design intent and agentic execution meet. To learn more, read up on our blog post “Agents, Meet the Figma Canvas.”

Start designing with agentic AI in Figma

An agentic framework turns a single AI response into a sustained, goal-driven workflow, and it’s already reshaping how design and development teams collaborate.

With the Figma MCP server, Figma Make, and Figma Design working together, product teams have the tools they need to take part in agentic workflows starting from the design side.

Here’s how to put it all together:

• Figma Design is the canvas where design decisions take shape, and it’s the foundation agents build from when they have access to your components, tokens, and styles.
• Figma Make gets you from product idea to interactive prototype using natural language, a natural entry point into agentic workflows without writing code.
• Dev Mode bridges design and code, giving engineers direct access to specs and Code Connect mappings, the same context the Figma MCP server surfaces to AI agents.
• Explore the Figma Community skills and templates for agentic workflows with the Figma MCP server.