AI Agents
/eɪ aɪ ˈeɪdʒənts/
What are AI Agents?
AI agents are artificial intelligence systems designed to autonomously take actions to accomplish goals. Unlike traditional chatbots that simply respond to queries, agents can:
- Use tools: Access external systems, APIs, databases, and applications
- Make decisions: Choose which actions to take based on context
- Execute multi-step tasks: Break down complex goals into actionable steps
- Learn and adapt: Improve performance based on feedback and outcomes
Think of the difference between a customer service chatbot (answers questions) and an AI agent (resolves the issue by accessing your account, issuing refunds, and updating records).
Why Agents Matter Now
The shift from chatbots to agents represents the next major evolution in AI:
From Response to Action: LLMs proved AI could understand and generate language. Agents prove AI can do work.
From Assistance to Autonomy: Copilots help humans work faster. Agents work independently on delegated tasks.
From Demos to Production: Enterprise adoption requires AI that integrates with existing systems and workflows.
How Agents Work
Core Components
- Language Model (Brain): Understands goals, reasons about tasks, generates plans
- Tools (Hands): APIs, functions, and systems the agent can invoke
- Memory: Context and state maintained across interactions
- Planning: Breaking complex goals into executable steps
- Feedback Loop: Learning from outcomes to improve
Example: Customer Support Agent
User: "I want to cancel my subscription and get a refund"
Agent thinking:
1. Look up user's account → [tool: database query]
2. Check subscription status → Active, 3 days old
3. Check refund policy → Eligible within 7 days
4. Cancel subscription → [tool: billing API]
5. Process refund → [tool: payment API]
6. Send confirmation → [tool: email API]
Agent: "Done! I've cancelled your subscription and processed
a full refund. You'll see $49 back in 3-5 business days.
Confirmation sent to your email."
Types of AI Agents
By Autonomy Level
| Level | Description | Example |
|---|---|---|
| Copilot | Suggests actions, human executes | GitHub Copilot |
| Semi-autonomous | Executes with human approval | Email draft + send |
| Autonomous | Executes independently | Background data processing |
By Domain
- Coding agents: Write, test, debug code (Claude Code, Cursor, Devin)
- Customer service agents: Handle inquiries end-to-end (Intercom Fin)
- Sales agents: Qualify leads, schedule meetings, follow up
- Research agents: Gather information, synthesize reports
- Operations agents: Process invoices, manage inventory, handle HR tasks
The Enterprise Opportunity
AI agents represent the next wave of enterprise productivity:
Scale: One agent can handle thousands of concurrent tasks Consistency: Agents don't have bad days or forget procedures 24/7 Availability: Work continues outside business hours Integration: Agents connect disparate systems automatically
Gartner projects that by 2028, 33% of enterprise software will include agentic AI, up from <1% in 2024.
Challenges
Trust: How much autonomy should agents have? Reliability: Agents can make mistakes with real consequences Security: Agents with system access are attack vectors Governance: Who's responsible when an agent makes a wrong decision?
The Human-Agent Future
The question isn't whether AI agents will do knowledge work—they already are. The question is how humans and agents will work together:
- Humans set goals, agents execute
- Humans handle exceptions, agents handle routine
- Humans provide judgment, agents provide scale
Related Reading
- Tool Use - How agents interact with external systems
- Enterprise AI - Where agents are deployed
- Knowledge Work Disruption - The trend agents are driving
Mentioned In
Rashid
"Cloud Code in its essence is an agent. It's a system that leverages an AI model to interact with its environment to achieve a user-defined objective. You give it a goal and AI can reason, plan, and execute on the actions."
Phillip (AI Executive) at 00:45:00
"2026 will be the year where you can buy a service of AI agents priced on the result—not 'you have five attempts' but 'here's an agent that solves customer support issues and I'm going to charge you for every successfully resolved case.'"
Jensen Huang at 00:15:00
"Agentic models that have the ability to reason, look up information, do research, use tools, plan futures, simulate outcomes—all of a sudden started to solve very very important problems."
Mustafa Suleyman at 00:25:00
"The next era is agents - AI that doesn't just respond but takes action on your behalf."
Rola (Tech42) at 00:30:00
"An agent has dynamic control flow devised by the LLM at runtime, whereas a workflow is predefined coded graphs. If you're going to take anything from this lecture, it's this."