In the previous article, “Understanding The Architecture of AI Agents: Perception,” we discussed how perception is the building block of an AI Agent’s capabilities. This article will take a deeper, more technical dive into how perception comes about.

Unlike traditional automation or chatbots that follow rigid scripts, Agentic AI can:

  • Handle ambiguity

  • Make intermediate decisions

  • React dynamically to changing inputs

  • Use tools and external APIs

  • Reflect on its actions and improve

However, for this intelligence to work smoothly, two technical concepts must be considered: State and memory.

What Is State?

State refers to the agent’s current understanding of the world or the working memory at a specific point in time.

Imagine you’re teaching a class. At any moment, your state includes:

  • What topic you are teaching

  • Which students asked questions

  • What examples you have already given

For an AI agent, the state might include:

  • The current user query

  • Intermediate outputs from previous steps

  • Which tools have already been used

  • Confidence levels or flags

Example in Business Context:

A customer support AI agent’s state could track:

  • Customer’s name and product

  • Current issue

  • Steps already taken to resolve it

  • Whether the issue was escalated

Without properly managing the state, the AI might repeat steps, forget previous actions, or give inconsistent responses.

What Is Memory?

While state is short-term and local to the current execution, memory refers to long-term storage—information that persists across sessions or after the agent finishes a task.

Memory is what allows agents to:

  • Remember your preferences across chats

  • Recall previous customer interactions

  • Learn and adapt over time

There are usually two types:

  1. Short-Term Memory: Active during the current session (like chat history).

  2. Long-Term Memory: Stored and retrieved over time (like a CRM or knowledge graph).

Why Business and Technical Leaders Must Understand This

Even though these are technical concepts, they have direct business implications. Misunderstanding or ignoring state and memory can lead to:

Poor Implementation

Real-World Consequence

Inconsistent state management

AI loops or gives contradictory responses

Poor memory handling

Agents forget users or make irrelevant recommendations

Leaky or wrong state

Sensitive data exposed or misused

Memory bloat or no expiry

Huge compute costs and slow response times

 

Real Example: Repeating Conversations in Customer Support

A B2B SaaS company integrated an AI support agent but failed to implement long-term memory. Every time a customer returned, the bot would ask the same onboarding questions. Frustration rose. Churn followed.

What Good Implementation Looks Like

A well-structured Agentic AI system:

  • Maintains state correctly: tracks where it is in the process

  • Uses memory effectively: recalls relevant history, forgets irrelevant details

  • Separates scope: does not confuse transient state with persistent memory

With LangGraph or similar frameworks:

  • State is usually modeled with TypedDict or equivalent schemas.

  • Reducers control how the state is updated when multiple steps happen.

  • Memory modules allow LLMs to remember and recall beyond a single session.

Wrapping Up: Strategy Meets Architecture

Agentic AI isn’t magic. It’s intelligent software built with care.

If you’re a:

  • Product Manager: Know how state and memory shape user experience

  • Engineering Leader: Ensure architecture cleanly separates state vs. memory

  • CXO: Recognize how poor implementation can cost trust, users, and money

By understanding these foundational ideas, you can make better decisions, ask smarter questions, and guide your team toward truly intelligent and reliable AI systems.