What are AI Agents?
Introduction
Artificial Intelligence (AI) and LLMs are constantly evolving, and one of the most exciting developments in recent times is the concept of AI Agents. In this blog post, we'll explore what AI Agents are, how they work, and discuss some key design patterns that are revolutionizing AI applications.
What are Agentic Workflows?
To understand AI Agent workflows, let's consider a real-world example: creating a comprehensive investment strategy for a client as a financial analyst.
Traditionally, using AI for this task might involve asking it to generate the entire strategy in one go. However, this approach lacks the nuance and adaptability of how expert financial advisors actually work.
In reality, a skilled financial advisor would follow an iterative process:
1. Analyze the client's financial situation and goals
2. Research current market trends and economic indicators
3. Draft an initial investment strategy
4. Simulate the strategy's performance under various market conditions
5. Revise the strategy based on the simulation results
6. Repeat steps 3-5 until the strategy is optimized
This iterative process is what we call an "agentic workflow." It mimics the approach of a skilled professional and, interestingly, produces much better results than a single-pass AI solution.
Key Design Patterns in AI Agent Workflows
There are three primary design patterns in AI Agent workflows:
Tool Use or Function Calling
Reflection
Multi-Agent Collaboration
Let's examine each of these in detail.
1. Tool Use or Function Calling
This pattern involves AI using external tools, typically accessed through APIs, to enhance its capabilities. For example:
When asked about the best-performing sectors for the next quarter, the AI might use financial data APIs to analyze recent trends.
If queried about a company's financial health, it could use tools to analyze financial statements and market sentiment.
This approach allows the AI to access up-to-date information and perform complex analyses beyond its built-in capabilities.
2. Reflection
Reflection is similar to an AI reviewing and critiquing its own work. In our financial analyst example:
The AI creates an initial portfolio allocation.
It's then prompted to review its own allocation.
It might conclude, "The current allocation is too heavily weighted in tech stocks. I should rebalance to reduce risk."
Based on this reflection, it adjusts the portfolio, potentially improving its performance and risk profile.
This self-review process allows for continuous improvement and refinement of the AI's output.
3. Multi-Agent Workflow
This pattern simulates a team of experts working together, each with specific expertise and roles. In creating a comprehensive financial plan, you might have:
An AI acting as a financial planner, setting overall strategy
Another as a tax expert, optimizing for tax efficiency
A risk management AI assessing and mitigating potential risks
An investment analyst AI selecting specific investments
A regulatory compliance AI ensuring all recommendations meet legal requirements
These "agents" collaborate, debate, and iterate to create a complete, personalized financial plan. This approach allows for a more holistic and well-rounded solution.
Conclusion
AI Agent workflows are revolutionizing the capabilities of AI applications. They enable more complex, nuanced, and expert-like problem-solving by allowing different components (or "lego pieces") to connect and work towards an end goal.
The future of AI applications isn't just about having the most powerful models – it's about using them in smarter, more efficient ways that are collaborative and iterative. AI Agent workflows are at the forefront of this evolution.
There's extensive ongoing research in this area, with many companies working on products that leverage these concepts. It's exciting to anticipate what patterns and standardizations will emerge as this field continues to develop.
What are your thoughts on AI Agents? How do you see them impacting various industries? Share your ideas in the comments below!
