AI agents arrived in 2025 – here’s what happened and the challenges ahead in 2026

2025-12-29 · Source: Artificial intelligence (AI) – The Conversation · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Cybersecurity & Data Privacy · Depth: Intermediate, medium

Summary

The year 2025 marked a significant shift in artificial intelligence, transitioning from generative AI to the widespread adoption of AI agents. These agents, defined by Anthropic as large language models capable of using software tools and taking autonomous action, moved from theoretical concepts to practical infrastructure. Key milestones included Anthropic's late 2024 release of the Model Context Protocol, enabling standardized tool connection, and Google's April 2025 introduction of the Agent2Agent protocol for inter-agent communication. Both protocols were later open-sourced to the Linux Foundation. This period also saw the emergence of open-weight models like DeepSeek-R1, intensifying global competition, and the proliferation of "agentic browsers" such as Perplexity's Comet and OpenAI's GPT Atlas, which actively participate in tasks like booking vacations. Workflow builders like n8n and Google's Antigravity further lowered the barrier for custom agent system creation.

Key takeaway

For CTOs and VPs of Engineering evaluating AI strategy, the shift to AI agents necessitates a re-evaluation of existing generative AI deployments. Your teams should prioritize integrating open protocols like the Model Context Protocol and Agent2Agent to build interoperable, proactive systems. Consider adopting agentic browsers and workflow builders to empower users with configurable AI tools, but also prepare for amplified security risks like indirect prompt injections and the need for robust governance frameworks.

Key insights

AI agents, unlike reactive generative AI, are proactive systems that perceive, reason, act, and learn with minimal human intervention.

Principles

• AI agents use LLMs as their reasoning engine.
• Chain of thought reasoning breaks complex tasks into steps.
• Open protocols foster interoperable AI ecosystems.

Method

AI agents operate in a perceive-decide-execute-learn cycle, often using large language models for chain of thought reasoning to break down complex tasks into smaller, logical steps before taking action.

In practice

• Use agentic browsers for multi-step tasks like booking travel.
• Employ workflow builders for custom agent systems.
• Integrate LLMs for agent decision-making and task planning.

Topics

• AI Agents
• Large Language Models
• Agentic Protocols
• AI Safety & Governance
• Open-weight Models

Code references

• features/copilot

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, Machine Learning Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial intelligence (AI) – The Conversation.