News from the AI & ML world
DeeperML
Justin Westcott,@AI News | VentureBeat
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AI agents are poised to revolutionize how we interact with the internet, moving beyond passive assistants to active participants authorized to act on our behalf. This shift necessitates a redesign of the web, transforming it from a human-centric interface to a machine-native environment optimized for speed, efficiency, and transactional capabilities. The current internet, designed for human eyes and fingers, is proving inefficient for AI, which requires structured data, clear intent, and exposed capabilities to navigate, decide, and transact effectively. This evolution will lead to a web where APIs become the new storefronts, prioritizing verifiable sources and trust over traditional user experience elements.
The development and deployment of AI agents face significant challenges, particularly in ensuring reliability and consistency within defined business processes. Existing agentic frameworks often fall short due to a lack of state, leading to unpredictable behavior and poor adherence to workflows. A recent survey highlighted that only 25% of AI initiatives are live in production, with hallucinations and prompt management being major obstacles. This indicates a need for robust evaluation processes and automated testing pipelines to address these issues, as traditional software QA methods may not fully apply to AI applications. The survey indicated that without robust evaluation, AI agents may not reach production or may not be sustainable long term.
An alternative approach, known as process calling, aims to create reliable, process-aware, and easily debuggable conversational agents. This method addresses the limitations of tool calling by incorporating state tracking and structured workflows. Companies achieving success with LLMs are prioritizing robust evaluation and moving beyond simple tool-based interactions. As AI agents become more prevalent, the internet will likely bifurcate into two webs: one designed for humans and another designed for machines. This machine-native web will feature faster protocols, cleaner metadata, and a focus on verifiable sources, ultimately reshaping the architecture of the internet to accommodate AI's unique requirements.
ImgSrc: venturebeat.com
References :
The development and deployment of AI agents face significant challenges, particularly in ensuring reliability and consistency within defined business processes. Existing agentic frameworks often fall short due to a lack of state, leading to unpredictable behavior and poor adherence to workflows. A recent survey highlighted that only 25% of AI initiatives are live in production, with hallucinations and prompt management being major obstacles. This indicates a need for robust evaluation processes and automated testing pipelines to address these issues, as traditional software QA methods may not fully apply to AI applications. The survey indicated that without robust evaluation, AI agents may not reach production or may not be sustainable long term.
An alternative approach, known as process calling, aims to create reliable, process-aware, and easily debuggable conversational agents. This method addresses the limitations of tool calling by incorporating state tracking and structured workflows. Companies achieving success with LLMs are prioritizing robust evaluation and moving beyond simple tool-based interactions. As AI agents become more prevalent, the internet will likely bifurcate into two webs: one designed for humans and another designed for machines. This machine-native web will feature faster protocols, cleaner metadata, and a focus on verifiable sources, ultimately reshaping the architecture of the internet to accommodate AI's unique requirements.
ImgSrc: venturebeat.com
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References :
- AI News | VentureBeat: Agent-based computing is outgrowing the web as we know it
- AI Accelerator Institute: What exactly is an AI agent – and how do you build one?
- Analytics Vidhya: Build a Conversational AI Agent with Rasa
Classification:
- HashTags: #AIAgents #TaskManagement #CustomerService
- Target: Professionals
- Product: AI Agents
- Feature: AI Agents
- Type: AI
- Severity: Informative