Agentic AI

Agentic AI refers to artificial intelligence systems that exhibit agency—the ability to act autonomously to achieve goals. Unlike traditional AI that simply responds to queries, agentic AI can:

• Plan: Break complex goals into actionable steps
• Reason: Make decisions based on context and available information
• Execute: Take actions using tools and APIs
• Adapt: Adjust plans based on feedback and results
• Persist: Work toward goals across multiple interactions

The term "agentic" emphasizes autonomy and goal-directed behavior. An agentic AI doesn't just answer questions—it accomplishes objectives.

Traditional AI (Reactive)

• Responds to single inputs
• No memory between interactions
• Cannot use external tools
• Human drives each step
• Example: "What's the weather?" → "It's 72°F"

Agentic AI (Autonomous)

• Pursues multi-step goals
• Maintains context and state
• Uses tools and APIs
• Self-directs toward objectives
• Example: "Plan my trip to Paris" → Books flights, hotels, creates itinerary, sends confirmations

The shift from reactive to agentic AI represents a fundamental change in how we interact with AI systems—from tools we use to assistants that work for us.

Reasoning and planning Agentic AI uses techniques like chain-of-thought prompting to reason through problems. It can decompose complex tasks into subtasks and determine the optimal sequence of actions.

Tool use Through function calling, agentic AI can interact with external systems—searching the web, querying databases, sending emails, or calling any API.

Memory and context Unlike stateless chatbots, agentic systems maintain memory across interactions. They remember user preferences, past actions, and conversation history.

Self-reflection Advanced agentic systems can evaluate their own outputs, detect errors, and retry with different approaches.

Multi-agent collaboration Some agentic architectures involve multiple specialized agents working together, each handling different aspects of a complex task.

Customer support agents An agentic customer support AI can: look up order status, initiate refunds, update shipping addresses, schedule callbacks, and escalate to humans—all autonomously based on the customer's needs.

Research assistants An agentic research AI can: search multiple sources, synthesize information, verify facts, generate reports, and iterate based on feedback.

Software development Tools like GitHub Copilot Workspace and Claude Code demonstrate agentic coding—understanding requirements, planning implementation, writing code across files, running tests, and fixing bugs.

Business process automation Agentic AI can handle entire workflows: processing invoices, routing approvals, updating records, and notifying stakeholders.

Personal assistants Future personal AI agents will manage email, schedule meetings, book travel, and handle routine tasks with minimal oversight.

1. Define clear goals What should the agent accomplish? Be specific about success criteria and constraints.

2. Design the tool set What capabilities does the agent need? Each tool should have a clear purpose and well-defined interface.

3. Implement the reasoning loop The core agent loop: perceive → think → act → observe. Use frameworks like LangGraph, AutoGen, or Chipp to structure this.

4. Add memory systems Short-term memory for conversation context, long-term memory for user preferences and learned information.

5. Build in guardrails Limit what the agent can do. Require confirmation for sensitive actions. Implement rate limits and scope restrictions.

6. Enable observability Log every decision and action. You need to understand why the agent did what it did, especially when things go wrong.

Reliability Agentic systems are only as reliable as their weakest step. Errors compound across multi-step tasks.

Unpredictability Autonomous systems can take unexpected paths. Testing all possible behaviors is nearly impossible.

Safety An agent with real-world capabilities could cause real-world harm. Prompt injection attacks are particularly concerning.

Cost Each reasoning step and tool call costs tokens and time. Complex tasks can become expensive.

Trust Users need to trust the AI to act on their behalf. Building and maintaining that trust requires transparency and consistent behavior.

Despite these challenges, agentic AI represents the future of AI applications—systems that don't just inform but act.