Building effective agents
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
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:- Begin with the simplest possible solution
- Measure performance rigorously
- Add complexity only when it demonstrably improves outcomes
- Focus on clear tool design and transparent agent behavior
- Test extensively in sandboxed environments with appropriate guardrails