Building Effective AI Agents: Patterns, Workflows, and Practical Implementation

AI agents are rapidly transforming how we build intelligent applications. As large language models (LLMs) mature, developers are moving beyond simple Q&A bots to create systems that can reason, plan, and act autonomously. But what does it take to build effective AI agents in practice? How do you move from theory to robust, production-ready workflows?

In this guide, we’ll synthesize key insights from Anthropic’s and OpenAI’s approaches to agentic systems. You’ll learn the foundational patterns, when to use each.

Table of Contents

• What Are AI Agents?
• Core Patterns in Agentic Systems
  • Prompt Chaining
  • Routing
  • Parallelization
  • Orchestrator-Worker
  • Evaluator-Optimizer Loop
• Best Practices for Building Agents
• Conclusion
• References

The Building Block of Agentic Systems

At the heart of every agentic system is a large language model (LLM) enhanced with augmentations—such as retrieval, tools, and memory. This “augmented LLM” is the foundational building block.

What are AI Agents?

An AI agent is more than just a chatbot. It’s a system that can:

• Understand complex inputs
• Reason and plan steps
• Use tools and external APIs
• Recover from errors and adapt
• Operate autonomously or with human feedback

Agents can range from simple workflow automations to fully autonomous systems that act independently over extended periods. The key is composability—building with simple, modular patterns that can be combined as needed.

Tip: Start simple. Only add complexity when it demonstrably improves outcomes.

Core Patterns in Agentic Systems

Agentic systems are best understood as workflows—ways to structure how LLMs and tools interact to solve tasks.

1. Prompt Chaining

What it is:

Breaks a complex task into a sequence of steps, where each LLM call processes the output of the previous one.

When to use:

Tasks that can be decomposed into clear, sequential subtasks When validation or review is needed at each step

Example Workflow: Personalized Coding Tutor

1. Curriculum Generator: Creates an outline based on a learning goal.
2. Quality Checker: Assesses if the outline matches the goal.
3. Lesson Writer: Expands each section into detailed lessons.

2. Routing

What it is:

Classifies an input and directs it to the most appropriate specialized agent.

When to use:

When specialized expertise improves results

Example Workflow: Multi-Language Coding Tutor

1. Routing Agent: Determines if the question is about Python, JavaScript, or SQL.
2. Specialist Agents: Each handles queries in their domain.

3. Parallelization

What it is:

Runs multiple LLM calls simultaneously, either on different subtasks or to generate diverse outputs for the same task.

When to use:

• Tasks that can be split for speed
• When multiple perspectives or attempts are valuable

Example Workflow: Coding Explanation Voting

• Run the same explanation agent three times in parallel.
• Use a picker agent to select the best result.

4. Orchestrator-Worker

What it is:

A central “orchestrator” agent dynamically breaks down tasks and delegates them to worker agents, then synthesizes the results.

When to use:

Complex, unpredictable tasks (e.g., codebase refactoring, research synthesis) When subtasks depend on the specific input

Example Workflow: Syllabus Creator

• Planner Agent: Generates search queries based on a topic.
• Search Agents: Execute queries in parallel.
• Writer Agent: Compiles results into a syllabus.

5. Evaluator-Optimizer Loop

What it is:

One agent drafts a response, another evaluates and provides feedback, and the process repeats until the output meets quality criteria.

When to use:

• When clear evaluation criteria exist
• For tasks that benefit from iterative refinement

Example Workflow: Coding Exercise Generator

• Draft Agent: Creates a coding exercise.
• Judge Agent: Reviews and provides feedback.
• Loop: Repeat until the judge approves.

Best Practices for Building Agents

1. Favor Simplicity: Use direct LLM API calls and simple patterns before reaching for complex frameworks.
2. Understand Your Tools: If using SDKs or frameworks, ensure you know what’s happening under the hood.
3. Design Clear Interfaces: Invest in well-documented tool and API definitions for your agents.
4. Test Extensively: Especially for autonomous agents, use sandboxed environments and guardrails.
5. Iterate and Measure: Continuously evaluate performance and refine your workflows.

What Is an Agent?

An agent is an LLM-driven system that can:

• Understand complex instructions or engage in interactive discussions
• Plan and operate independently, often using tools and environmental feedback in a loop
• Pause for human feedback at checkpoints or when encountering blockers
• Terminate upon completion or when a stopping condition is met

Agents are ideal for open-ended problems where the number of steps can’t be predicted in advance and where autonomy is required. While agents can handle sophisticated tasks, their implementation is often straightforward—typically just an LLM using tools in a loop, guided by environmental feedback.

References:

1. Anthropic’s Guide on Building Effective Agents

2. Blog on Building Effective Agents