In this issue:

• 19 posts about autonomous agents concepts, research and frameworks.

💡 ML Concept of the Day: A Summary of Our Long Series About Autonomous Agents and a New Series Announcement

Today, we conclude our series about autonomous agents. This has been one of TheSequence’s most ambitious series. Over the last weeks, we had covered some of the fundamental concepts, research and technology in the autonomous agents space. I hope this has contributed to your understanding of this new space.

Autonomous agents are one of those concepts that everyone has a different definition for.  In AI theory, an autonomous agent is an AI program with the ability to execute actions in their environment. This contrasts with basic models that can produce outputs but not modify the environment. Although action taking is a key theoretical component for autonomous agents its hardly sufficient. Other relevant capabilities are key building blocks of autonomous agents in the LLM era:

• Reasoning

• Planning

• Memory

• Tool Integration

• Human Feedback

• ….

Different frameworks have outlined different feature sets for autonomous agents. However, there are a few building block that can be consistently considered as a strong foundation of any autonomous agent application.  Most of the capabilities of autonomous agents can be considered variations of the following key areas:

• Profiling: To perform actions effectively, agents should assume a given role such as domain experts, coders, assistants or any relevant profile that outlines key behavioral characteristics. Profiling helps to set those roles in order to guide the behavior of an agent.

• Memory: Memory helps an agent persists information captured from an environment and reuse it on different actions. Memory capabilities help an agents learn and evolve over time.

• Knowledge: Agents knowledge don’t need to be constrained to what has been captured in an LLM, they can start off with specific knowledge of a given domain. A knowledge module will give agents knowledge sets that can be accessed in order to accomplish a task.

• Reasoning/Planning: Planning is, arguably, the most important capability of any successful agent application. Reasoning and planning enables agents with the ability to break down problems into specific steps that can be orchestrated to accomplish a goal.

• Actions: Integrating with tools and APIs in order to accomplish task is an essential feature of autonomous agents. A module for supporting action integration should be part of any agent framework.

Let’s recap the contents of this series:

1. Edge 381: Introduces our series about autonomous agents. It discusses the AGENTS paper from ETH Zurich and the famous BabyAGI framework.

2. Edge 383: Discusses the core capabilities of autonomous agents. Reviews a paper from Google and Stanford University that outlines an agent that can simulate human behavior. And it dives into the Crew AI framework for building autonomous agents.

3. Edge 385: Outlines the main two schools of thoughts for agents implementations: LLM-based vs. computer vision-based. Discusses Adept AI’s Fuyu-8B which powers its agent platform and the super popular AutoGen framework from Microsoft.

4. Edge 387: Discusses the ideas behind agents that can master the use of tools. Reviews UC Berkeley’s amazing research about Gorilla, a LLM fine-tuned in tool usage. It also reviews Microsoft’s TaskWeaver code-first agent for analytics workflows.

5. Edge 389: Dives into the concept of large action models(LAM) including the LAM research from the Rabbit r1 team. It also reviews the MetaGPT framework for building autonomous agents.

6. Edge 391: Gets into the topic of agent function calling. Reviews UC Berkeley’s research about a compiler for parallel function calling and dives into the Phidata framework for building agents.

7. Edge 393: The series starts exploring planning in autonomous agents. It covers the research behind Allen AI’s ADaPT planning method. It also provides an overview of the XLANG’s autonomous agent framework.

8. Edge 395: Explores task-decomposition methods in autonomous agents. Reviews Google’s influential reasoning+act(ReAct) paper and provides an introduction to the Bazed framework for building autonomous agents.

9. Edge 397: Reviews the complex topic of multi-plan selection in autonomous agents including some research from Allen AI in this area. It also dives into the SuperAGI framework.

10. Edge 399: Discusses external-aid planning in autonomous agents. Reviews the research behind IBM’s Simplan that combines classical planning with LLMs. Dives into the Langroid framework for building autonomous agents.

11. Edge 401: Dives into the popular reflect and refinement methods in autonomous agents. Reviews the Reflextion paper by Northwestern University about agent planning. And the AgentVerse framework for multi-agent task planning.

12. Edge 403: Explores autonomous agents’ potential for memory-based planning. Reviews the  TravelPlanner benchmark for planning in autonomous agent. It also covers the amazing MemGPT framework.

13. Edge 405: Start exploring memory-augmentation in autonomous agents. Reviews Google’s ambitious research claiming that memory-augmented LLMs can be computationally universal. It also reviews the Camel framework for building autonomous agents.

14. Edge 407: Dives into the concept of short-term memory for autonomous agents. Reviews Google’s recent Inifini-Attention method for unlimited context windows in LLMs and dives into the AutoGPT framework.

15. Edge 409: Explores long-term memory in autonomous agents. It covers Microsoft’s LONGMEM research about long-term memory augmentation in LLMs and Pinecone vector database platform.

16. Edge 411: Discusses the topic of episodic memory in autonomous agents. Reviews the Larimar research for episodic memory in LLMs by Princeton University and IBM. It also divers into the Chroma vector database stack.

17. Edge 413: Explores the concept of semantic memory in autonomous agents. Explores Meta AI’s MA-LLM memory augmented model for video understanding. It also reviews the Qdrant vector database.

18. Edge 415: Reviews procedural memory in autonomous agents. It covers the research behind the JARVIS-1 memory-augmented multimodal model. It also reviews the Zep framework for long-term memory planning in LLMs.

19. Edge 417: Dives into the emerging concept of multi-agent systems. It reviews the research behind Alibaba’s AgentScope framework for multi-agent interactions. It also covers the popular LangGraph’s framework for building multi-agent systems.

19 installments! I really hope you enjoyed it. For the next series, we will dive into one of the hottest topics in generative AI: state space models(SSMs) and whether they are a viable alternative to transformers.