Making AI More Human: The Power of Agentic Systems
Written by
Jack Teitel, Sr. AI/ML Scientist
Published
December 13, 2024
As AI advances, large language models (LLMs) like GPT-4 have amazed us with their ability to generate human-like responses. But what happens when a task requires more than just straightforward answers? For complex, multi-step workflows, agentic systems represent a promising frontier, offering LLMs the ability to mimic human problem-solving processes more effectively. Let’s explore what agentic systems are, how they work, and why they matter.
What are Agentic Systems?
Agentic systems go beyond traditional one-shot prompting — where you input a single prompt and receive a single response — by introducing structured, multi-step workflows. These systems break down tasks into smaller components, use external tools, and even reflect on their outputs to iteratively improve performance. The goal? Higher-quality responses that can tackle complex tasks more effectively.
Why Traditional LLMs Fall Short
In a basic one-shot prompt scenario, an LLM generates a response token by token, from start to finish. This works well for simple tasks but struggles with:
- Complex Reasoning: Tasks requiring planning or iterative refinement.
- Knowledge Limitations: Access to outdated or incomplete information.
- Error Handling: Lack of self-correction during generation.
For example, if you ask a standard LLM to write an essay or debug a piece of code, it might produce a flawed output without recognizing or correcting its mistakes.
One method of correcting these limitations is to use multi-shot prompting, where the user interacts with the LLM, sending multiple prompts. By having a conversation with the LLM, a user can point out mistakes and prompt the LLM to provide better and more refined output. However, this still requires the user to analyze the output, suggest corrections, and interact with the LLM more than just the original prompt, which can be rather time-consuming.
One-Shot Prompting
- Single prompt → single response
- Response generated one token at a time
- Tokens generated in order, from start to finish
- Fast response time
Multi-Shot Prompting
- Conversation with LLM
- Includes context/history of previous conversation
- Responses can be refined based on human feedback
- Takes longer to get a final response
- Requires human interaction/intervention for each prompt
Categories of Agentic Systems
Agentic systems address these limitations by employing four key strategies:
1. Reflection
Reflection enables an LLM to critique its own output and iteratively improve it. For instance, after generating code, a reflection step allows the model to check for bugs and propose fixes automatically.
Example Workflow:
- Prompt the LLM to write code for a task.
- Ask it to review the code and identify any issues.
- Have it correct the code based on the reflection, iterating until no errors remain.
2. Tool Use
Tool use allows LLMs to call external APIs or perform actions beyond simple token generation (the only action within scope of a traditional LLM). This is essential for tasks requiring access to real-time information via web search or needing to perform specialized functions, such as running unit tests or querying up-to-date pricing.
Example Workflow:
- Prompt the LLM to write code.
- Execute unit tests through a CI/CD API.
- Refine the code based on the test results.
3. Planning
Planning helps LLMs tackle complex tasks by breaking them into smaller, manageable steps before execution. This mirrors how humans approach large problems, such as developing an outline before writing an essay.
Example Workflow:
- Generate a plan for solving a coding task.
- Execute each step of the plan sequentially.
- Combine the results to complete the task.
4. Multi-Agent Systems
Multi-agent systems distribute tasks among specialized agents, each with a defined role (e.g., planner, coder, reviewer). These specialized agents are often different instances of an LLM with varying system prompts to guide their behavior. You can also utilize specialized agents that have been specifically trained to perform different tasks. This approach mirrors teamwork in human organizations and allows each agent to focus on its strengths.
Example Workflow:
- A planner agent creates a step-by-step plan.
- A coder agent writes the code.
- A reviewer agent identifies issues.
- The coder agent corrects code based on issues found
- The system iterates steps 2-4 until the desired result is achieved.
Why Agentic Systems Matter
Agentic systems offer several advantages:
- Better Performance: Combining reflection, planning, and tool use can significantly enhance results, even when using older or smaller models like LLama-8B.
- Explainability: The explicit multi-step nature provides transparency into how the model arrived at its solution — crucial for fields like healthcare or finance.
- Flexibility: Agentic systems allow you to mix and match elements like reflection, planning, and tool use to suit your needs. You can deploy highly specialized agents for targeted tasks or more general agents for broader use cases, adapting seamlessly to different scenarios.
Practical Applications of Agentic Systems
Coding Assistance
In software development, agentic systems can write code, test it, and debug autonomously. For example:
- A planning agent outlines the steps required to build a program.
- A coding agent writes the code.
- A tool-use agent runs the code in a test environment.
- A reviewer agent identifies bugs and suggests improvements.
Business and Healthcare
In domains where decision-making requires transparency and reliability, agentic systems excel. By providing clear reasoning and detailed workflows, they can:
- Assist in medical diagnoses by integrating data from multiple sources.
- Support business forecasting with transparent, step-by-step analysis.
- Provide assurance in output accuracy through outlining specific logical steps/reasoning used to reach conclusions.
Real Time Information Analysis
Many businesses, such as finance, stock trading/analysis, e-commerce and retail, social media and marketing, rely on real-time information as a vital component of their decision-making. For these applications, agentic systems are necessary to extend the knowledgebase of stock LLMs beyond their original training data
- Analyze current stock market trends in near real time.
- Track and analyze current social media trends.
- Evaluate return rates, customer interaction, and competitor pricing in near real time.
Creative Collaboration
From generating marketing campaigns to designing product prototypes, multi-agent systems can simulate entire teams, each agent offering specialized input, such as technical accuracy, customer focus, or business strategy.
Implementing Agentic Systems
Building agentic workflows may sound complex, but tools like LangGraph simplify the process. LangGraph, developed by the creators of LangChain, allows you to define modular agent workflows visually, making it easier to manage interactions between agents. Any code or LLM can act as a node (or agent) in LangGraph.
For example, if working in Snowflake, LangGraph can be combined with Snowflake Cortex to create an agentic workflow leveraging native Snowflake LLMs, RAG systems, and SQL generation, allowing you to build complex agentic workflows in the same ecosystem as more traditional data analytics and management systems while ensuring strict data privacy and security.
For simpler use cases, platforms like LlamaIndex also support agentic capabilities, particularly when integrating data-focused workflows.
The Future of Agentic Systems
As research evolves, agentic systems are expected to remain relevant, even as base LLMs improve. The flexibility of agentic workflows ensures they can be tailored to specific domains, making them a valuable tool for automating complex, real-world tasks. In addition, as base LLMs improve, you can keep your same agentic workflows in place, but swap out the individual agents for the improved LLMs, allowing you to easily improve the overall system performance. In this way, agentic systems not only improve accuracy of traditional LLMs, but can easily scale/adapt to the current rapidly changing LLM ecosystem.
In the words of AI pioneer Andrew Ng, agentic systems represent “the next big thing” in AI. They offer a glimpse into a future where AI doesn’t just respond — it reasons, plans, and iterates like a true digital assistant.
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