How to build effective AI Agents

Dont invest in AI Agents without reading this Anthropic report

Here's a comprehensive breakdown of the research...

Often most automation that we use daily can be easily done with a few LLMs and APIs

and that's something Anthropic too is trying to convey

This research shares what they think are AI agents and why you should not always build one.

📌 We recently covered Google's whitepaper where we broke down how Google's vision of AI Agents.

Now, after reading both Google's and Anthropic's take - It is safe to say that Google's paper was more on What AI Agents.

Whereas Anthorpic's take is much more on Why and When should you should use AI Agents.

Here's a brief breakdown from their research:

📌 Agents vs. Workflows: Agents are dynamic systems where LLMs direct their own processes and tool usage, while workflows follow predefined paths. Agents shine when flexibility and decision-making are key.

📌 Core Parts of AI Agents:

1. Augmented LLMs

2. Tools used by the Agumented LLMs

3. Environment

4. Memory

📌 Key Workflow for Agents:

• Prompt Chaining: Breaking tasks into sequential steps for higher accuracy.

• Routing: Directing inputs to specialized tasks for better performance.

• Parallelization: Running tasks simultaneously for speed or diverse outputs.

• Orchestrator-Workers: A central LLM delegating tasks to worker LLMs.

• Evaluator-Optimizer: Iterative refinement by multiple processes for polished results.

📌 When to Use Agents:

You don't always need to use Agents, Often your automation can be easily done using a few automation workflow tools like N8N and other commercial tools.

Here are a few problems where you should use AI Agents:

• Open-ended problems require flexibility.

• Tasks where decision-making scales with complexity.

• Environments with trusted autonomy and clear feedback loops.

📌 Few Frameworks given by Anthropic to Consider:

• LangGraph (LangChain)

• Amazon Bedrock's AI Agent Framework

• Rivet and Vellum for GUI-based workflow building

💡 Key Takeaway:

• Success isn’t about building the most complex system—it’s about building the right system.

• Start simple, measure performance, and add complexity only when it demonstrably improves outcomes.

• Sometimes without understanding the core aspect of an agent we redundantly pile up too much code from a few frameworks which Often leads to redundant code being piled up without understanding.

• Hence they specifically aimed this research to bring more clarity to people who are trying to build AI agents for their businesses.

Author

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