How Agentic AI Thinks, Plans, and Gets Stuff Done (Technically Speaking)

We’ve talked about what agentic AI is: AI that doesn’t just wait to be told what to do, but takes initiative, plans its next move, and learns from what worked (or didn’t). But let’s go deeper.

This post is for anyone who wants to understand how agentic AI actually works—not in metaphor, but in architecture. This is where we pop the hood, trace the wires, and follow the loop (quite literally!).

For a less technical pov, you can read the post here!

What Makes AI "Agentic"?

Traditional AI is reactive. You prompt it, it replies. One shot. Done.

Agentic AI is different. It’s goal-driven and runs in a continuous loop: it perceives what’s happening, plans its next move, takes action, checks if it worked, and then tries again or moves on. That feedback loop is what lets it keep going even when things get messy or change midstream.

Step 1: Perception Layer

This is the part where the agent takes in its environment. This involves interpreting a user goal or real-world input into something actionable. Under the hood, this step may include prompt parsing, language model preprocessing, and use of libraries like LangChain to load and normalize documents or inputs.

Inputs can be anything: A user’s voice command ("Plan a product launch"), a flooded inbox, or even sensor data from a robot.
The challenge: Raw data is messy. The agent must parse ambiguity (e.g., "Contact the client" → "Which client? Pull from CRM or email history?").

Technically, this is the input ingestion phase—where unstructured or semi-structured data gets converted into structured context the agent can use. Modern agents use tools like knowledge graphs (to map relationships) and multimodal models (to process text, images, or speech) to ground their understanding.

Before an agent can act, it needs to understand its environment. Think of this like a chef walking into the kitchen and checking what ingredients are available, what tools are clean, and what orders are pending.

Step 2: Memory + State Management

Agentic AI requires memory, unlike stateless LLMs that forget everything after one prompt. This includes:

Short-term memory: What’s happened in the current loop
Long-term memory: User preferences, past actions, success/failure history

Retrieval-Augmented Generation (RAG) is commonly used to pull relevant chunks of data into the context window based on semantic similarity. This enables the agent to reference past actions, outcomes, and external documents while staying contextually aware.

A good chef doesn’t ask the same questions twice. They remember that the rosemary ran out yesterday, that the oven takes a while to heat, and that table five always wants extra sauce. Agentic AI does the same. It stores short-term and long-term memory to stay efficient and context-aware.

Step 3: Planning & Reasoning Module

Planning is where agentic AI transforms goals into executable steps. This involves:

Task decomposition: breaking a large goal into smaller, manageable subtasks the system can reason through.
Prioritization: determining the order of operations, often based on task dependencies or contextual urgency.
Contingency planning: anticipating failure points or alternate paths if a given approach doesn't work.
Tool selection: mapping specific tasks to the most appropriate tools or APIs available, depending on their input/output structure and the overall plan.

These planning mechanics rely on a variety of structured reasoning techniques designed to extend an LLM's capabilities beyond single-shot responses. One common method is Chain-of-Thought (CoT) prompting, where the model is explicitly encouraged to reason step-by-step before arriving at an action. Another is ReAct, which interleaves reasoning and acting by prompting the model to think, act, observe the result, and iterate.

More sophisticated approaches like Tree of Thoughts allow the agent to generate multiple reasoning branches and explore different paths before selecting the optimal one. In collaborative or multi-role environments, frameworks like CrewAI and AutoGen enable multi-agent orchestration, assigning different agents to specific roles like planner, executor, or validator to manage complex workflows.

These modules are capable of generating intermediate subtasks, invoking relevant tools at each step, and revising their strategy dynamically if a task fails or if better alternatives emerge during execution.

The chef now decides what to cook and in what order—which dishes need to be started early, what can cook while something else rests, and how many burners are free. This is where agentic AI breaks a goal into tasks, prioritizes steps, selects tools, and builds contingency plans.

Step 4: Tool Use + Execution

This is the stage where agentic AI shifts from planning to doing. It carries out actions like calling APIs, scraping data using browser tools, automating entries in Google Sheets, triggering workflows through platforms like Zapier or Make, or composing and sending emails directly.

These actions are powered by different execution mechanisms eg:

Function calling / tool invocation APIs (e.g., OpenAI’s tool-use architecture)
Toolkits like LangChain agents or Hugging Face Transformers agents
Plug-ins for SaaS products, web browsers, or cloud services

Each tool is defined with a schema (input/output) and invoked conditionally based on the current plan. The execution is often done asynchronously to allow retries or cancellations. Crucially, agents learn which tools work best. Over time, agentic systems can learn which tools are most effective in a given context. For instance, they might learn that Slack messages get faster replies than email within a specific team, or that the Shopify API becomes unreliable after 3 PM. This tool-awareness makes the execution layer smarter and more adaptive.

Now it’s time to do.!The chef grabs ingredients, fires up the stove, mixes, sautés, and plates. They know which knife to use for each job, and when to preheat the oven versus blend a sauce. The agent selects the right tool for each job, executes it asynchronously, and handles retries or failures along the way.

Step 5: Reflection + Feedback Loops

After it acts, agentic AI doesn’t just move on—it checks its work. Did the task succeed? Did it return the expected output? Was the goal met? This is handled via:

Result validators (hard-coded or LLM-evaluated)
Scoring mechanisms (e.g., similarity to expected output, user feedback)
Meta-prompts where the agent critiques its own output or reruns logic if it detects failure

If failure is detected, the loop restarts: new plan, new tool, revised approach. This feedback loop is the key architectural upgrade. It allows the system to adapt on the fly and behave more like a reasoning entity than a static model.

Finally, the chef tastes the food. If something’s too salty or undercooked, they fix it. If the plating looks off, they redo it. Feedback shapes the final dish. Agentic AI does this too. If the result doesn’t meet expectations, it loops back to replan or adjust until the job is done.

Why This Architecture Matters

Agentic AI systems don’t just complete tasks—they navigate uncertainty. They:

Persist through failed attempts
Make strategic decisions
Use multiple tools across steps
Learn from feedback
Adapt to new goals and constraints

The shift from reactive to agentic AI isn’t just technical—it’s philosophical. Traditional AI is a hammer; agentic AI is a contractor. It doesn’t wait for you to say "now hit the nail"—it assesses the house, gathers materials, and adjusts when the wood splits.

We’re not just building smarter tools. We’re building collaborators. And now you know what’s under the hood!

Bonus: Agentic AI in Action!

Want to see agentic AI in action? Check out this demo where a friend, Jerop, shows a code assistant that doesn't just suggest fixes—it executes them. Unlike static tools, it remembers context, debugs by actually running code, and even handles terminal commands. Now that's an AI teammate!