AI Agent Architecture: Explained Like You’re Building It

This is where the agent receives input.

Inputs can be: Text queries from users, sensor data (in robotics), system logs, API outputs, web pages, etc.

The perception module takes raw input and extracts meaning.

For language-based agents, this means:

✔️ Tokenizing the input

✔️ Extracting intent

✔️ Pulling entities or commands

✔️ Converting it to structured data

Example:

Input → “Turn on the living room lights”

Output →

That structured form flows into the next layer.

Agents need memory to function properly.

This includes — short-term memory (context from the current session), long-term memory (past conversations or facts), and external memory (databases, APIs, logs)

Modern agents often combine:

✔️ Vector databases for semantic search (using embeddings)

✔️ Key-value stores for caching

✔️ Knowledge graphs for structured reasoning

✔️ Scratchpads for temporary notes during planning

In LLM agents, memory helps — recall past steps in a workflow, track goals, and reuse prior knowledge.

This memory store is usually exposed as a service the planner can query.

This is the brain of the agent. It decides what steps to take.

There are two common styles:

● Static planners that use a decision tree or rule set

● Dynamic planners that use a language model to plan in real-time

Let’s say your input is:

“Generate a report on revenue for Q1, send it to finance, and schedule a review meeting.”

The planner would:

✔️ Break this into subtasks

✔️ Check what tools it needs

✔️ Decide the right sequence

✔️ Pass instructions to the execution module

A planner might generate a task tree like:

Language model–based planners might use prompting to break things into steps. Others use symbolic AI or even classical planning algorithms.

After planning, the agent needs to pick a specific action.

This is different from planning. Planning is about options. Decision-making is about choosing.

Here, the agent might score available paths using:

● Utility functions

● Preference models

● Cost vs reward trade-offs

● Goal alignment checks

Some agents use reinforcement learning models to rank options. Others use simpler scoring logic like “lowest cost, highest confidence.”

The output is a decision to take one step, right now.

This is the muscle of the system.

Once the agent picks an action, this layer runs it. That could mean:

✔️ Sending an HTTP request

✔️ Running a local function

✔️ Querying a database

✔️ Launching a subprocess

✔️ Writing to a file

In tool-based agent architectures (like LangChain or CrewAI), tools are wrapped functions the agent can call.

Example:

Agents are given toolkits. The execution layer picks the right tool, passes parameters, and waits for the result.

If the result comes back with errors, it loops back to the planner to revise the plan.

Not every architecture includes this yet — but it’s the future.

This layer helps the agent:

✔️ Review what it did

✔️ Learn from outcomes

✔️ Update memory with results

✔️ Improve future performance

There are several approaches:

● Reinforcement Learning – reward-based training over time

● Human-in-the-loop feedback – users grade the agent’s choices

● Self-critique loops – the agent critiques its own past actions

This block enables agents to go from reactive to adaptive.