Building Effective Agents

Based on Anthropic’s guide to building effective AI agents, focusing on simplicity, composability, and practical patterns.

Introduction

Building effective AI agents requires understanding when and how to add complexity to your LLM applications. According to Anthropic’s experience working with dozens of teams across industries, the most successful agent implementations use simple, composable patterns rather than complex frameworks.

We enjoyed reading building effective AI agents by Anthropic’s engineering team. So we adapted the key points to work with Latitude projects.

Core Principles

Show Start Simple

Find the simplest solution possible and only increase complexity when needed. This might mean not building agentic systems at all. Often, optimizing single LLM calls with retrieval and in-context examples is sufficient.

Show Consider Trade-offs

Agentic systems often trade latency and cost for better task performance. Consider when this trade-off makes sense for your use case.

Show Choose the Right Pattern

• Workflows offer predictability and consistency for well-defined tasks
• Agents are better when flexibility and model-driven decision-making are needed at scale

The Augmented LLM (Foundation)

The basic building block is an LLM enhanced with:

• Retrieval: Access to external information with Latitude tools and third-party MCP integrations
• Tools: Ability to perform actions with calling LLM tools
• Memory: Context retention across interactions with RAGs

What are Agents?

Anthropic categorizes agentic systems into two main types:

Workflows

Systems where LLMs and tools are orchestrated through predefined code paths

Agents

Systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks

Workflow Patterns

Chaining

An example of a workflow pattern where tasks are decomposed into sequential steps, each step building on the previous one.

Routing

Classifies input and directs it to specialized follow-up tasks.

Parallelization

LLMs can sometimes work simultaneously on a task and have their outputs aggregated programmatically

Orchestrator-Workers

A central LLM dynamically breaks down tasks, delegates to worker LLMs, and synthesizes results.

Evaluator-Optimizer

One LLM generates responses while another provides evaluation and feedback in a loop.

Autonomous Agents

Autonomous Agents

Agents operate independently using tools based on environmental feedback in loops. They’re ideal for open-ended problems where you can’t predict the required number of steps or hardcode a fixed path.

Key Takeaways

Success in the LLM space isn’t about building the most sophisticated system—it’s about building the right system for your needs. Start with simple prompts, optimize them with comprehensive evaluation, and add multi-step agentic systems only when simpler solutions fall short.

The most effective approach is to:

1. Begin with the simplest possible solution
2. Measure performance rigorously
3. Add complexity only when it demonstrably improves outcomes
4. Focus on clear tool design and transparent agent behavior
5. Test extensively in sandboxed environments with appropriate guardrails