Agentic AI

From Chatbots to Agents

Traditional AI systems are reactive—they respond to inputs but don't initiate action. Agentic AI represents a paradigm shift: systems that can autonomously set goals, make plans, and take actions in the world to achieve those goals.

An agentic AI doesn't just answer questions. It can break down complex tasks, use tools to gather information, write and execute code, and iterate on its approach based on results.

Key Capabilities

Agentic systems typically combine several capabilities:

Goal decomposition: Breaking a high-level objective into manageable subtasks.

Planning: Determining sequences of actions to achieve goals.

Tool use: Interfacing with external systems—web browsers, code interpreters, APIs, databases.

Memory: Maintaining context and learning from past actions across long sessions.

Self-correction: Recognizing errors and adjusting approach.

Current Examples

By 2025-2026, agentic AI has moved from proof-of-concept to production:

• Coding agents: Claude Code, Devin, and GitHub Copilot Workspace autonomously write, test, debug, and deploy code—used daily by developers and engineering teams
• Computer-use agents: Claude Computer Use and similar systems operate software interfaces like a human, navigating browsers, filling forms, and executing multi-step workflows
• Research agents: Systems that search the web, read documents, synthesize information, and produce structured reports with citations
• Enterprise agents: AI systems integrated into business workflows for customer support, data analysis, and process automation
• Multi-agent systems: Specialized agents collaborating via protocols like MCP (Model Context Protocol), enabling tool use and inter-agent coordination at scale

Safety Implications

Agentic AI raises significant safety concerns:

Reduced oversight: Autonomous systems may take many actions between human checkpoints, making it harder to catch errors or misalignment.

Compounding errors: A mistake early in an action sequence can propagate, with the agent pursuing increasingly wrong paths.

Unintended side effects: Agents optimizing for goals may find unexpected ways to achieve them, including ways that harm users or third parties.

Capability amplification: Agents can leverage tools to have real-world effects far beyond what the base model could achieve alone.

The Path to AGI

Many researchers see agentic capabilities as a key component of AGI. A system that can autonomously pursue goals, learn from experience, and use tools to extend its capabilities begins to resemble general intelligence—especially as the underlying models become more capable.