AI Agents have become one of the most popular tech trends of 2024, but they are still pretty new and require a lot of improvement. This year we saw an outstanding research and improvements in agents that even made us rethink the overall concept. From Frameworks to Surveys, we have categorized the list this year as there was a great number of novel research papers coming from top companies and Universities.
Let’s take a look at our collection of research papers that made AI Agents even more interesting this year:
Frameworks:
1. Magentic-One by Microsoft
Magentic-One is an updated version of Microsoft’s Autogen framework, designed to create a generalist multi-agent system for solving open-ended web and file-based tasks across various domains.
Key features:
- Multi-agent architecture for handling complex tasks
- Ability to process web and file-based inputs
- Generalist approach for versatility across domains
The system likely employs a coordinated set of specialized agents, each focusing on different aspects of task solving, such as information retrieval, reasoning, and output generation. We can infer that Magentic-One probably uses a hierarchical structure where a central coordinator manages task distribution among various specialized agents.
2. Agent-oriented planning in a Multi-Agent system
This framework introduces a novel approach to planning in multi-agent systems using a Meta-agent architecture. The system is designed to enhance coordination and decision-making among multiple AI agents.
Key features:
- Meta-agent architecture for overseeing planning
- Improved coordination among multiple agents
- Enhanced decision-making capabilities
We can envision a system where a meta-agent oversees and coordinates the planning activities of individual agents. This meta-agent has a global view of the task and can optimize the overall planning strategy by considering the strengths and limitations of each agent in the system.
3. KGLA by Amazon
Amazon’s KGLA (Knowledge Graph-enhanced Agent) framework is designed to improve knowledge retrieval across various domains. This system leverages knowledge graphs to enhance the capabilities of AI agents.
Key features:
- Integration of knowledge graphs with AI agents
- Improved knowledge retrieval capabilities
- Applicability across multiple domains
The KGLA architecture likely consists of several key components:
- Knowledge Graph: A structured representation of domain knowledge
- Agent Interface: Allows agents to query and interact with the knowledge graph
- Retrieval Mechanism: Efficiently extracts relevant information from the graph
- Reasoning Module: Combines retrieved knowledge with agent capabilities
This integration allows agents to access and utilize structured knowledge more effectively, potentially improving their performance on complex tasks that require extensive domain knowledge.
4. Harvard’s FINCON
FINCON is an LLM-based multi-agent framework developed by Harvard researchers, specifically designed for diverse financial tasks. It employs conversational verbal reinforcement to enhance agent performance.
Key features:
- Specialized in financial domain tasks
- Multi-agent architecture
- Conversational verbal reinforcement learning
The FINCON architecture likely includes:
1. Multiple Specialized Agents: Each focusing on different aspects of financial tasks
2. Conversational Interface: Allows agents to communicate and learn from each other
3. Verbal Reinforcement Module: Provides feedback to improve agent performance
4. Task Coordinator: Manages the distribution and integration of subtasks
This framework’s focus on financial tasks and its use of conversational reinforcement makes it particularly well-suited for complex financial decision-making and analysis scenarios.
5. OmniParser for Pure Vision-Based GUI Agent
OmniParser introduces a multi-agent approach specifically designed for UI navigation in GUI-based AI agents. This system aims to improve the ability of AI agents to interact with graphical user interfaces.
Key features:
- Specialized for GUI navigation
- Multi-agent system for parsing visual elements
- Enhanced AI agent interaction with graphical interfaces
The OmniParser architecture likely includes:
- Visual Element Detector: Identifies UI components in images
- Semantic Interpreter: Understands the function and context of detected elements
- Navigation Planner: Determines optimal paths for GUI interaction
- Action Executor: Carries out planned interactions with the GUI
This system’s focus on visual parsing and GUI navigation makes it particularly valuable for tasks involving automated software testing, user interface analysis, and the development of more intuitive AI assistants.
6. AutoRestTest by IBM
AutoRestTest is a framework developed by IBM for testing REST APIs using multi-agents and semantic graphs. This system aims to improve the efficiency and effectiveness of API testing processes.
Key features:
- Specialized for REST API testing
- Utilizes a multi-agent approach
- Incorporates semantic graphs for improved understanding
The AutoRestTest architecture likely consists of:
- API Parser: Extracts API specifications and structures
- Semantic Graph Generator: Creates a graph representation of API relationships
- Test Case Generator: Designs test scenarios based on the semantic graph
- Multi-Agent Executor: Runs tests using multiple specialized agents
- Result Analyzer: Interprets test outcomes and identifies issues
This framework’s use of semantic graphs and multi-agent execution allows for more comprehensive and intelligent API testing, potentially uncovering issues that traditional testing methods might miss.
7. AIOps by Microsoft
AIOpsLab is a comprehensive framework developed by Microsoft for designing, developing, and evaluating autonomous AIOps (AI for IT Operations) agents. This system aims to advance the field of AI-driven IT operations.
Key features:
- Holistic approach to AIOps agent development
- Supports design, implementation, and evaluation phases
- Focus on autonomous operation in IT environments
The AIOpsLab architecture likely includes:
- Agent Development Environment: Tools for creating and training AIOps agents
- Simulated IT Infrastructure: Replicates real-world IT environments for testing
- Scenario Generator: Creates diverse operational challenges
- Performance Evaluation Module: Assesses agent effectiveness
- Feedback Loop: Allows for iterative improvement of agents
This framework provides a standardized platform for advancing the field of AIOps, potentially leading to more efficient and reliable IT operations management.
8. Graph Reader by Alibaba
Graph Reader is a framework proposed by Alibaba to enhance the long-context abilities of LLMs using graph-based agents. This approach aims to improve LLMs’ performance on tasks requiring an understanding of extensive context.
Key features:
- Graph-based representation of long texts
- Agent-driven exploration of the graph
- Enhanced long-context understanding for LLMs
The Graph Reader architecture includes:
- Text-to-Graph Converter: Transforms long texts into graph structures
- Graph Explorer Agent: Navigates the graph to extract relevant information
- LLM Interface: Integrates graph-based knowledge with LLM capabilities
- Answer Generator: Produces responses based on graph exploration and LLM processing
This innovative approach allows LLMs to effectively process much longer contexts by leveraging graph structures and targeted exploration, potentially overcoming the limitations of traditional sequential processing.
9. DynaSaur by Adobe and University of Maryland
DynaSaur is a framework for LLM agents that can dynamically create and compose actions online. This system aims to enhance the flexibility and adaptability of AI agents in various tasks.
Key features:
- Dynamic action creation and composition
- Online learning and adaptation
- Enhanced flexibility for LLM agents
While the specific architecture is not detailed in the search results, we can infer that DynaSaur likely includes:
- Action Generator: Creates new actions based on task requirements
- Composition Module: Combines actions to form complex behaviours
- Online Learning Component: Adapts agent behaviour in real-time
- Task Analyzer: Determines appropriate actions for given situations
This framework’s ability to dynamically generate and compose actions allows for more versatile and adaptive AI agents, potentially improving performance across a wide range of tasks and environments.
10. ShowUI: One Vision-Language-Action Model for GUI Visual Agent by Microsoft
ShowUI is a vision model developed by Microsoft researchers to improve the identification of UI elements for GUI-based AI agents. This system aims to enhance the ability of AI agents to interact with graphical user interfaces.
Key features:
- Specialized for GUI element identification
- Integration of vision, language, and action modelling
- Improved performance for GUI-based AI agents
The ShowUI architecture likely includes:
- Visual Encoder: Processes GUI images to identify elements
- Language Model: Interprets textual information in the GUI
- Action Predictor: Determines appropriate interactions with GUI elements
- Multi-modal Fusion Module: Combines visual, textual, and action information
This model’s focus on GUI element identification and interaction could significantly improve the performance of AI agents in tasks involving software testing, user interface analysis, and automated GUI navigation.
11. Automated Design of Agentic Systems by the University of Columbia
This framework focuses on automatically inventing novel building blocks and combining them to create innovative agents. It aims to advance the field of AI agent design by introducing automation and creativity into the process.
Key features:
– Automated invention of agent components
– Novel combination of building blocks
– Creation of innovative AI agents
While the specific architecture is not provided, we can envision a system that includes:
1. Component Generator: Creates new agent building blocks
2. Compatibility Analyzer: Determines how components can be combined
3. Agent Assembler: Constructs agents from compatible components
4. Performance Evaluator: Assesses the effectiveness of created agents
5. Evolutionary Optimizer: Iteratively improves agent designs
This approach to automated agent design could lead to the discovery of novel and highly effective AI architectures, potentially advancing the field beyond human-designed systems.
Experimentation & Analysis:
1. Can Graph Learning improve planning in LLM based Agents?
Microsoft’s research demonstrates how graph learning can enhance planning capabilities in LLM-based agents, particularly when using GPT-4 as the core model. This groundbreaking study provides empirical evidence for integrating graph structures into agent planning systems.Key features:
- Advanced graph learning integration with LLMs
- GPT-4 core model optimization
- Enhanced planning capabilities for AI agents
The architecture includes:
- Graph Learning Module: Processes and analyzes graph structures
- Planning Optimizer: Enhances agent decision-making
- GPT-4 Integration Layer: Connects graph learning with language model
- Performance Analysis System: Measures and validates improvements
This research significantly advances the field of AI planning systems by demonstrating the practical benefits of graph learning integration.
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2. Generative Agent Simulations of a thousand people- By Stanford and Google Deepmind
A collaborative breakthrough between Stanford and Google DeepMind achieved remarkable results in simulating 1,000 unique individuals using just two hours of audio data.Key features:
- Large-scale behavioral simulation
- Efficient audio data processing
- Advanced generative modeling
The architecture includes:
- Audio Processing Engine: Analyzes and extracts behavioral patterns
- Simulation Generator: Creates individual behavioral models
- Scaling Module: Manages large-scale simulations
- Validation System: Ensures accuracy of simulated behaviors
This breakthrough opens new possibilities for large-scale behavioral modeling and simulation.
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3. Bytedance’s Bug Fixing Analysis
Bytedance conducted comprehensive testing to identify the most effective LLMs for automated bug fixing, providing valuable insights for implementing agent-based code repair systems.Key features:
- Automated bug detection and analysis
- LLM performance comparison framework
- Real-time code repair capabilities
The architecture includes:
- Bug Detection Engine: Identifies and classifies code issues
- LLM Integration Layer: Connects multiple language models
- Code Analysis Module: Evaluates repair suggestions
- Performance Monitoring System: Tracks repair success rates
This research significantly advances automated code maintenance and quality assurance processes.
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4. Google DeepMind’s improving Multi-Agent debate Systems with Sparse Communication Topology
Research into multi-agent debate systems with sparse communication topology revealed improved performance despite limited information sharing.Key features:
- Sparse communication optimization
- Enhanced agent debate protocols
- Efficient information sharing mechanisms
The architecture includes:
- Communication Topology Manager: Optimizes information flow
- Debate Protocol Engine: Manages agent interactions
- Information Sharing Module: Controls data exchange
- Performance Analysis System: Measures communication efficiency
This breakthrough advances our understanding of efficient multi-agent communication systems.
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5. Improving AI Agents with Symbolic Learning
A comprehensive examination of progress and challenges in LLM-based Multi-Agent Systems, focusing on problem-solving and world simulation applications.Key features:
- Extensive analysis of current LLM-MA systems
- Problem-solving capability assessment
- World simulation application review
The architecture includes:
- Analysis Framework: Evaluates system capabilities
- Comparison Engine: Assesses different approaches
- Challenge Identification System: Maps current limitations
- Future Direction Mapper: Projects development paths
This survey provides crucial insights for future development of LLM-based multi-agent systems.
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Surveys:
1. LLM-based Multi-Agents Survey
A comprehensive examination of progress and challenges in LLM-based Multi-Agent Systems (LLM-MA), focusing on applications in problem-solving and world simulation scenarios.Key features:
- Comprehensive analysis of LLM-MA systems
- Progress tracking and challenge identification
- Application-focused evaluation framework
The architecture includes:
- System Analysis Framework: Evaluates current LLM-MA implementations
- Challenge Mapping Module: Identifies key obstacles and limitations
- Application Assessment Engine: Reviews real-world applications
- Future Direction Predictor: Projects development trajectories
This survey provides crucial insights for advancing LLM-based multi-agent system development.
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2. LLM-brained GUI Agents Survey
An extensive analysis of the evolution and complexity of GUI-based agents across various domains, highlighting key developments and challenges.
Key features:
– Historical evolution analysis
– Cross-domain complexity assessment
– Implementation pattern evaluation
The architecture includes:
1. Evolution Tracker: Maps development progression
2. Complexity Analysis Engine: Evaluates implementation challenges
3. Domain Comparison Module: Assesses cross-domain applications
4. Pattern Recognition System: Identifies successful implementations
This survey significantly contributes to understanding GUI agent development patterns and future directions.
3. The Dawn of GUI Agent: A case study-use of the Sonnet 3.5
A comprehensive analysis of Anthropic’s Computer use capabilities across multiple domains, providing practical insights into real-world applications.
Key features:
– Multi-domain usability testing
– Performance metrics analysis
– Real-world application assessment
The architecture includes:
1. Usability Testing Framework: Evaluates interface interactions
2. Performance Measurement System: Tracks success metrics
3. Domain Adaptation Module: Assesses cross-domain capabilities
4. Implementation Guide: Provides practical deployment insights
This case study offers valuable insights into practical GUI agent implementation.
4. Taxonomy for AgentOps by CSIRO
A systematic categorization of AI Agent operations, providing standardized terminology and operational frameworks.
Key features:
– Standardized terminology framework
– Operational classification system
– Implementation guidelines
The architecture includes:
1. Terminology Database: Maintains standardized definitions
2. Classification Engine: Organizes operational categories
3. Relationship Mapper: Links related concepts
4. Implementation Framework: Guides practical application
This taxonomy establishes crucial standards for AI agent development and deployment.
5. Practises for Governing Agentic AI Systems by OpenAI
A comprehensive framework outlining seven key principles for implementing safe and accountable AI agent systems in business environments.
Key features:
– Safety-first approach
– Accountability frameworks
– Business implementation guidelines
The architecture includes:
1. Safety Assessment Module: Evaluates potential risks
2. Accountability Framework: Ensures responsible deployment
3. Implementation Guide: Provides practical steps
4. Monitoring System: Tracks compliance and performance
These guidelines establish essential standards for responsible AI agent deployment.
Benchmarks for AI Agents:
1. Partner: A Benchmark for Planning and Reasoning in Multi-Agent tasks.
A comprehensive evaluation framework for assessing planning and reasoning capabilities in multi-agent systems, focusing on human-agent coordination.
Key features:
– Human-agent coordination metrics
– Planning capability assessment
– Household activity simulation
The architecture includes:
1. Task Generation Engine: Creates test scenarios
2. Coordination Assessment Module: Evaluates interaction quality
3. Performance Metrics System: Measures success rates
4. Analysis Framework: Provides detailed insights
This benchmark provides valuable metrics for improving human-agent coordination systems.
2. CRM Arena by Saleforce
A sophisticated benchmark system for evaluating AI agents in customer support scenarios using real-life sandbox methodology.
Key features:
– Real-world scenario simulation
– Customer support focus
– Sandbox testing environment
The architecture includes:
1. Scenario Generator: Creates realistic customer cases
2. Response Evaluation System: Assesses agent performance
3. Metrics Analysis Engine: Measures effectiveness
4. Integration Testing Module: Validates CRM compatibility
This benchmark advances the evaluation of AI agents in customer service applications.
These represent significant advancements in addressing the challenges of long-context processing, multi-agent systems, and automated AI design. By leveraging techniques such as graph-based representations, dynamic action composition, and automated component generation, these approaches are pushing the boundaries of what’s possible with AI agents and large language models.
This year only marks the beginning of what the future of AI agents will look like. As the agents become more complex and generalist to the point where we have very little to no human intervention, We think it is very exciting to see what 2025 holds for Agents.
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