Agentic AI Design Patterns Every Developer Should Know

Beyond Prompt Engineering

If 2024 was the year of prompt engineering, 2025-2026 is the year of agentic design patterns. The shift from single-turn LLM calls to multi-step autonomous agents requires a new set of architectural patterns.

As developers, we're used to design patterns like MVC, Observer, and Strategy. Agentic AI brings its own set of patterns that are equally important to master.

The Core Agentic Patterns

1. ReAct (Reasoning + Acting)

The ReAct pattern interleaves reasoning and action. The agent thinks about what to do, takes an action, observes the result, and reasons again.

Thought: I need to find the bug in the authentication module.
Action: Search for "auth" in the codebase.
Observation: Found 3 files related to authentication.
Thought: Let me read the main auth handler first.
Action: Read auth-handler.ts
...

This is the foundation of most modern AI agents. It's simple, effective, and debuggable.

2. Plan-and-Execute

Instead of acting step-by-step, this pattern first creates a complete plan, then executes each step. This works well for well-defined tasks where the full scope is knowable upfront.

When to use: Migrations, refactoring, feature implementations with clear requirements.

When to avoid: Exploratory tasks where the path depends on what you discover.

3. Tool Use

Agents become dramatically more capable when they can use tools - web search, code execution, file manipulation, API calls. The pattern involves:

Defining available tools with clear descriptions
Letting the agent choose which tool to use based on context
Returning tool results for the agent to interpret

4. Multi-Agent Orchestration

Complex tasks often benefit from multiple specialized agents working together:

Planner Agent: Breaks down the task
Coder Agent: Writes the code
Reviewer Agent: Reviews and suggests improvements
Tester Agent: Writes and runs tests

Each agent has focused expertise, and an orchestrator manages the workflow.

5. Reflection and Self-Correction

The agent evaluates its own output and iterates. This is what separates good agents from great ones:

Generate → Evaluate → Critique → Improve → Repeat

This pattern dramatically reduces errors and improves output quality.

Implementing Agentic Patterns in Practice

Starting Simple

Don't jump straight to multi-agent systems. Start with a single ReAct agent that can use 2-3 tools. Get the basics right:

Clear system prompts
Well-defined tool schemas
Good error handling
Token budget management

Scaling Up

Once your single agent works reliably, consider:

Adding memory (conversation history, vector stores)
Implementing plan-and-execute for complex workflows
Splitting into specialized sub-agents
Adding human-in-the-loop checkpoints for critical decisions

The Frontend Perspective

As a frontend developer, I see enormous potential in agentic AI for:

Component generation: Describe a UI, get a working React component
Accessibility auditing: Agents that crawl your app and fix a11y issues
Performance optimization: Automated bundle analysis and code splitting
Design-to-code: Converting Figma designs to production components

Key Takeaways

Start with ReAct - it's the most versatile pattern
Tools are force multipliers - every tool you add expands capabilities exponentially
Multi-agent is powerful but complex - use it when single-agent hits its limits
Always add guardrails - agentic systems need boundaries to be safe
Observe and iterate - log everything, measure quality, improve continuously

In my next post, I'll walk through building a multi-agent system from scratch using modern frameworks. Stay tuned!