Introduction: How AI Agents Learn, Adapt, and Complete Tasks
AI agents are essentially large language models (LLMs). Traditional LLMs generate responses rooted in the data they were trained on and are bounded by knowledge and reasoning constraints. Today, sophisticated agentic technology can leverage backend tool integration to access real-time information, streamline workflows, and automate tasks to achieve complex goals effectively.
AI agents can now adapt to user expectations by learning from past interactions and planning future actions to provide comprehensive and personalised responses, even without human intervention.
Achieving user goals involves 3 key stages:
1. Goal Initialisation and Planning
While AI agents can operate autonomously in decision-making, they still rely on human-defined goals, tools and environments. Given the user’s objectives and available resources, these agents generate a series of tasks and subtasks, refining their outputs at each step to achieve complex goals efficiently.
2. Resources for Agent Reasoning
AI agents make decisions based on perceived information, yet often lack the complete knowledge and resources needed to accomplish complex tasks. To bridge this gap, they utilise tools like external datasets, web searches, APIs and other agents to fill in missing information and update their knowledge base, enabling continuous reassessment and self-correction during task execution.
This interaction between tools allows AI agents to tackle a wider range of tasks compared to traditional AI models, providing more general-purpose assistance.
3. Reinforcement Learning
AI agents improve the accuracy of their responses through feedback from other AI agents and human-in-the-loop (HITL) input.
For instance, if a user representing a crypto VC fund is presented a prediction of the next big Web3 project, the agent saves the user's feedback and learned information to refine future performance, adapting to unique preferences.
Feedback from multiple agents minimises the need for user direction, while feedback from users ensures better alignment of outcomes with intended goals.
This iterative refinement process enhances the agent’s reasoning and accuracy, while storing past solutions in a knowledge base helps to avoid repeated mistakes.
Conclusion
AI agents mark a major step towards the agentic web. By integrating with tools and real-time resources while learning from feedback, they can dynamically reassess and adapt their strategies to navigate complex goals with higher precision and personalisation. They are set to transform the digital assistant landscape, offering innovative solutions across various domains and enhancing productivity in unprecedented ways.
*Disclaimer: The information provided on this blog does not constitute investment advice, financial advice, trading advice, or any other form of professional advice. aelf makes no guarantees or warranties about the accuracy, completeness, or timeliness of the information on this blog. You should not make any investment decisions based solely on the information provided on this blog. You should always consult with a qualified financial or legal advisor before making any investment decisions.
What's aelf Ventures?
aelf Ventures is the investment arm of aelf, a high-performance Layer 1 AI blockchain platform that offers builders and users advanced AI functionalities and cutting-edge infrastructure. With a dedicated fund of $50 million, aelf Ventures is focused on empowering Layer 1 blockchain projects and various aspects of the Web3 ecosystem, such as DeFi, GameFi, NFT, and those looking to make the transition from Web2 to Web3.
Till date, aelf Ventures has invested in projects such Crystal Fun and Confiction Labs (pka. Mythic Protocol), and is actively incubating promising ventures within the ecosystem such as Portkey, eBridge, Forest NFT Marketplace, AwakenSwap, eWell, and BeanGoTown.
Find out more about aelf and stay connected with our community:
Website | X | Telegram | Discord