Your AI has agency — here’s how to architect its frontend

Written by Rosario De Chiara✏️

As more teams rush to integrate AI agents into their products, frontend developers often face a frustrating gap: the back-end intelligence is evolving fast, but the UI patterns to support it are lagging behind.

Traditional input-output interfaces struggle to keep up with agents that can reason, revise, and act autonomously. Agentic AI refers to AI systems capable of reasoning, acting autonomously, and adapting to their environment or task. As these agents shift from passive tools to dynamic collaborators, the design patterns underpinning them become increasingly critical.

This article outlines six emerging architectural patterns that define how these agents are built and operated. These patterns enable powerful applications in search, automation, coding, and more. We also explore how these back-end capabilities shape UI/UX on the front end — highlighting opportunities and challenges for frontend developers working with intelligent systems.

Agentic AI architecture patterns

These six core design patterns form the technical backbone of agentic AI systems. Each offers a different take on how intelligent agents reason, act, collaborate, or retrieve information — and they shape how front-end teams will need to support, visualize, and work with these capabilities.

ReAct agent — think, act, repeat

The ReAct (Reasoning and Acting) model is a foundational pattern where agents alternate between reasoning steps — using large language models (LLMs) — and taking actions, such as querying a tool or external service. This looped process of “thinking and doing” mimics human problem-solving and serves as the blueprint for most current AI agents.

It’s also the baseline architecture for using LLM-based agents — the LLM “reasons” about how to solve a problem and then executes that plan via a tool. In a sense, this serves as the abstract blueprint for implementing all the various agentic architectures described below.

CodeAct agent — Python as the execution engine

Developed by Manus AI, this architecture enables agents to directly generate and execute Python code — rather than relying on rigid JSON-based actions. This opens the door to more complex reasoning and execution.

While JSON merely conveys structured data, Python allows for both data representation and manipulation. This architecture enables more complex reasoning and dynamic task execution. For example, when you ask ChatGPT to create a PowerPoint presentation, it may generate and execute Python code to build the file, which you can then download. This ability to operate beyond pre-defined functions creates more flexible, autonomous workflows.

Modern tool use — cloud-scale orchestration

In this pattern, agents interact with cloud infrastructure through lightweight execution standards such as MCP (Multi-Cloud Platform). MCP allows agents to access and orchestrate tools across hundreds of cloud platforms, enabling massive scalability and integration without requiring deep infrastructure expertise.

From a dev standpoint, this allows developers to build thin layers of orchestration logic on top of powerful, distributed services — making the agent far more extensible with minimal overhead:

Self-reflection — agents with a conscience

Inspired by metacognitive strategies, self-reflective agents can review and critique their own outputs to improve performance over time. Typically, this involves a second LLM acting as a kind of internal reviewer or conscience. The agent evaluates its own proposed actions, iteratively refining them until they meet a quality threshold.

This not only boosts accuracy but introduces a mechanism for learning from failure — moving closer to human-like problem-solving loops:

Multi-agent workflow — many agents, one goal

Rather than relying on a single generalist agent, this pattern involves multiple specialized agents collaborating on a task. Each agent brings a distinct skillset — often through tailored prompts or tools — and contributes to a shared output. This is especially valuable during development and debugging, as responsibilities are clearly divided.

For instance, when a specific output seems off, it’s easier to isolate which agent handled that step. This pattern also opens the door to more modular, composable agent systems — much like microservices in software architecture.

Agentic RAG — memory-enhanced generation

This pattern combines retrieval mechanisms with generative capabilities. Agents access external knowledge — typically from a vector database or API — then synthesize that data into meaningful responses.

By blending search with generation, Agentic RAG systems produce more accurate and informed responses. We have a detailed article on RAG that explores its various flavors in depth.

Designing UI for agentic architectures

Designing user interfaces for Agentic AI systems requires rethinking the typical request-response model. These agents don’t just answer queries — they explain, revise, and coordinate with other systems.

That means the front end must be transparent, adaptive, and collaborative. Below are practical examples of how different Agentic patterns shape front-end design and user interaction flows:

ReAct agents

Use a dual-pane conversational interface:

• One pane displays the agent’s internal reasoning process (e.g., plans, hypotheses)
• The other pane shows external actions taken (e.g., API calls, file generation)

This creates a clear, step-by-step user journey and avoids a “black-box” experience. Including animated “thinking” states or progress indicators enhances transparency and sets user expectations.

CodeAct agents

CodeAct UIs resemble live coding environments:

• A top panel shows auto-generated Python code
• A bottom panel displays execution results (graphs, logs, etc.)

Include contextual buttons like “Explain this code” or “Edit and rerun” to preserve user control. Because the architecture depends on code as the core execution format, code visibility and editability become central to the UX — especially for technical users.

Modern tool use agents

These agents require modular, task-based layouts:

• A sidebar lists all integrated tools, showing real-time status or usage metrics
• The main panel shows a timeline of actions taken by the agent

This allows users to trace decisions and understand how tools are leveraged. The challenge lies in representing a wide variety of tools without cluttering the interface.

Self-reflective agents

Here, versioning and revision history are critical:

• Use timeline-based views to show drafts, critiques, and final outputs
• Inline annotations (like Google Docs comments) can show what changed and why

Let users accept, reject, or comment on revisions — creating a feedback loop that mirrors how humans co-edit and iterate.

Multi-agent workflows

UIs must visualize agent collaboration and task flow:

• Use process diagrams or activity maps to show which agent did what, when
• Expandable cards or accordions can help users dive deeper into each agent’s output

This transparency empowers users to interact with specific agents — e.g., reassigning tasks or retrying underperforming steps.

Agentic RAG interfaces

Best served by split views:

• One pane shows retrieved context (e.g., vector search results)
• The other displays the generated response, with footnotes or tooltips linking to source material

Include a “Show Sources” toggle to help users distinguish between factual grounding and hallucination. This is especially useful in technical Q&A, tutoring, or research scenarios.

A final note on Chat UIs

Altogether, these UI patterns show how the front-end must evolve in tandem with agentic back-end architectures — not only to support functionality, but also to create a collaborative and transparent relationship between humans and intelligent systems.

Keep in mind that all these variants can ultimately boil down to a chat-based UI, but we think that using a chat-based UI can create inertia that we might want to avoid.

Quick comparison of agentic patterns

For your quick reference, I’ve built this table comparing all six architectures:

**PATTERN**	**CORE CAPABILITY**	**WHAT MAKES IT DIFFERENT**	**IDEAL USE CASE**	**UI IMPLICATIONS**
ReAct	Reason + act loop	Alternates thinking with tool use	Search agents, email assistants	Two-pane view to expose reasoning and actions
CodeAct	Live code execution	Uses code generation as action	Report generation, API interaction	Console-style output, editable code cells
Modern Tool Use	Cloud orchestration	Agent-to-tool abstraction across services	Enterprise workflow automation	Modular dashboard with real-time status
Self-Reflection	Output self-critique	Agent uses feedback loops to improve	Content validation, QA	Versioned views with inline annotations
Multi-Agent	Specialized agent collaboration	Multiple agents with focused roles	Product design, document generation	Process flow with agent activity highlights
Agentic RAG	Contextual retrieval	Combines memory search with generation	Knowledge assistants, tutoring	Split view with sources and generated output

Actual tradeoffs and limitations

Agentic architectures are powerful, but they’re not magic. With great autonomy comes great complexity. Here’s what you should keep an eye on:

• Latency — More reasoning means more steps, which means more waiting. Users don’t love watching your AI think out loud unless it’s worth the wait
• Observability — Multi-step agents can be a nightmare to debug without clear logs, traceability, or visibility into the toolchain
• Security — Giving agents the keys to your APIs or file system is risky business. Guardrails, scopes, and runtime restrictions are non-negotiable
• Cost and compute — Self-reflection, multi-agent workflows, and memory retrieval often stack multiple LLM calls. That invoice adds up — fast
• User trust — If users can’t tell what the agent did or why, they won’t just be confused — they’ll be gone

Designing around these tradeoffs isn’t optional — it’s part of the job. Think of it as agentic gravity. You can’t defy it, but you can build smartly within it.

Conclusion

These six design patterns represent the current frontier in Agentic AI development. From reflective loops to multi-agent collaboration, they’re reshaping how frontend and backend systems interact.

As these systems evolve, we expect to see more autonomous agents with long-term memory, agent-specific GUIs with persistent state, and real-time feedback loops where users and agents co-adapt.

UI systems that allow for granular transparency, reversibility, and human override will likely define successful implementations in production. And as these patterns mature, expect to see hybrid models emerge — blending reasoning, retrieval, execution, and critique into tightly integrated systems.

For developers, especially those working on the front end, understanding these patterns isn’t just helpful — it’s essential to building the next generation of intelligent, user-aligned applications.

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