Agentic AI

Agentic AI

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1. What Is Agentic AI?

Agentic AI refers to AI systems that can act autonomously. They don’t just answer questions. They plan, make decisions, use tools, and execute multi-step tasks on their own. Think of it as the difference between a chatbot that responds to prompts and an AI that actually does things for you.

A regular LLM takes an input and gives an output. An agentic system takes a goal, breaks it down into steps, decides which tools to use, handles errors along the way, and keeps going until the job is done.

Traditional AI:  Input → Output
Agentic AI:      Goal → Plan → Act → Observe → Adjust → Act → ... → Done

2. Why It Matters Now

We’ve had LLMs for a few years, but agentic AI is what makes them actually useful in the real world. A few things came together:

• Tool use. Models can now call APIs, run code, search the web, read files, and interact with databases.
• Planning & reasoning. Models got better at breaking complex tasks into logical steps.
• Memory. Short-term (conversation context) and long-term (stored knowledge) let agents maintain state across interactions.
• Cost & speed. Inference got cheap enough to let agents loop and retry without breaking the bank.

This is the shift from AI as a tool you use to AI as a worker you delegate to.

3. How Agentic Systems Work

Most agentic AI follows a loop:

1. GOAL      → What needs to be done
2. PLAN      → Break it into sub-tasks
3. ACT       → Execute a step (call a tool, write code, query an API)
4. OBSERVE   → Check the result
5. REFLECT   → Did it work? What's next?
6. REPEAT    → Loop until the goal is met

The key components:

Component	Role
LLM (Brain)	Reasoning, planning, decision-making
Tools	APIs, code execution, web search, file I/O, databases
Memory	Context window + external storage for long-term recall
Orchestrator	Controls the loop: when to act, when to stop, when to ask for help

4. Single Agent vs. Multi-Agent

Single agent. One LLM handles everything. Good for straightforward tasks.

Multi-agent. Multiple specialized agents collaborate, each handling a different part of the problem. For example:

• Researcher agent. Gathers data from APIs and the web
• Analyst agent. Processes and interprets the data
• Writer agent. Drafts a report or summary
• Reviewer agent. Checks for errors and quality

Multi-agent systems are more complex to build but handle harder problems better because each agent can be tuned for its specific role.

User Goal
   ↓
Orchestrator Agent
   ├── Researcher Agent → tools: web search, API calls
   ├── Analyst Agent    → tools: code execution, data analysis
   ├── Writer Agent     → tools: text generation, formatting
   └── Reviewer Agent   → tools: validation, fact-checking
   ↓
Final Output

5. Real-World Use Cases

Agentic AI is already showing up everywhere:

• Coding assistants. Not just autocomplete, but agents that read your codebase, plan changes across multiple files, run tests, and fix errors autonomously (like what GitHub Copilot is becoming).
• Data analysis. Give an agent a dataset and a question. It writes SQL, runs queries, builds charts, and explains the results.
• Customer support. Agents that look up order info, process refunds, escalate to humans when needed, all without a script.
• Research. Agents that search papers, summarize findings, identify gaps, and draft literature reviews.
• DevOps. Agents that monitor systems, diagnose incidents, and apply fixes or roll back deployments.
• Personal assistants. Schedule meetings, draft emails, book flights, manage to-do lists, all by chaining tools together.

6. Frameworks & Tools

The ecosystem is moving fast. Some of the frameworks I’ve been looking at:

Framework	What It Does
LangChain / LangGraph	Build agent workflows with tool use, memory, and branching logic
CrewAI	Multi-agent orchestration with role-based agents
AutoGen (Microsoft)	Multi-agent conversations and collaboration
OpenAI Assistants API	Built-in tool use, code interpreter, file search
Claude Tool Use	Native function calling and multi-step reasoning
Semantic Kernel	Microsoft’s SDK for building AI agents with plugins

The common pattern across all of them: give the LLM access to tools, let it decide when and how to use them, and wrap it in a loop with error handling.

7. Challenges & Risks

Agentic AI isn’t all upside. A few things to watch out for:

• Hallucination loops. An agent makes a wrong assumption, acts on it, and compounds the error across multiple steps. By the time you catch it, the damage is done.
• Cost blowup. Every loop iteration costs tokens. A poorly designed agent can burn through API credits fast by retrying or going in circles.
• Safety & control. An autonomous agent with access to production databases, APIs, or external systems can do real harm if it misunderstands the goal. Guardrails are essential.
• Debugging is hard. When an agent takes 15 steps to reach an answer, tracing why it did what it did is way harder than debugging a single prompt-response.
• Over-autonomy. Sometimes you don’t want the AI to just go ahead and do things. Human-in-the-loop checkpoints are important for high-stakes decisions.

8. Opportunity & Threat — Two Sides of the Same Coin

This is the part I keep going back and forth on. Agentic AI is clearly a massive opportunity, but the threat side is just as real, and honestly, they’re inseparable.

The opportunity:

• Leverage. A single person can now do what used to take a team. An engineer with the right agents can ship features, run data pipelines, monitor systems, and handle ops, all without hiring five more people. Small teams punch way above their weight.
• Speed. Tasks that took hours (research, analysis, code review, report generation) can happen in minutes. The feedback loop between idea and execution shrinks dramatically.
• Accessibility. People who aren’t developers can build things. Natural language becomes the new interface. A marketing person can query a database, a founder can prototype an app, a student can build something real.
• New business models. Agents-as-a-service, autonomous workflows, AI-native products that couldn’t exist before. The design space just got way bigger.

The threat:

• Job displacement is real. Let’s not sugarcoat it. If an agent can do 80% of a junior analyst’s job, companies will hire fewer junior analysts. The same applies to support reps, QA testers, content writers, and entry-level developers. The roles won’t disappear overnight, but the headcount will shrink.
• Skill erosion. When agents handle the tedious work, people stop learning how to do it themselves. That’s fine until the agent breaks and nobody knows how to fix the underlying problem. We risk raising a generation of operators who can prompt but can’t build.
• Concentration of power. The people and companies that build and control agents gain an outsized advantage. If agents become the primary way work gets done, whoever owns the agent layer owns the value chain.
• Security surface. Every tool an agent can access is a tool it can misuse. An agent with database write access, API keys, and the ability to send emails is a security incident waiting to happen if the guardrails fail.
• Race to the bottom. When everyone has access to the same AI agents, the work they produce converges. Differentiation shifts from execution to taste, strategy, and the quality of your data, which not everyone has.

The honest take? I think the people who treat agentic AI as a tool to amplify their own judgment will do well. The ones who blindly delegate everything to it without understanding what’s happening underneath will get burned. The technology is neutral. It’s leverage, and leverage amplifies both skill and incompetence.

9. Where I Think This Is Going

A few predictions:

• Agents will become the default interface. Instead of clicking through UIs, you’ll tell an agent what you want and it’ll handle the rest. The app becomes the agent.
• Specialization over generalization. The best agents won’t be “do everything” systems. They’ll be narrow, domain-specific agents that are really good at one thing (finance, code, operations, etc.).
• Agent-to-agent communication. Agents will start talking to other agents, forming workflows that span organizations and systems. MCP (Model Context Protocol) and similar standards are early steps toward this.
• Trust and verification. As agents do more, we’ll need better ways to audit what they did, why, and whether it was correct. Explainability will matter more than ever.
• The “human in the loop” becomes the differentiator. The most valuable skill won’t be prompting an agent. It’ll be knowing when to override it. Domain expertise, judgment, and taste become more important, not less.

The way I see it, we’re at the point where AI stops being something you talk to and starts being something that works for you. That’s a big shift, and it’s happening faster than most people realize. Whether that’s exciting or terrifying probably depends on which side of the leverage you’re on.