Agentic AI: Part 3- Key Terms You Should Know
In Part 2, we explored how AI agents have progressed from simple chatbots to intelligent systems that can plan, act, and adapt on their own. If you haven’t read it yet, it’s a good place to start to understand the foundation we’re building on.
In this post, we go a step further.
To really grasp Agentic AI, it’s important to understand the key concepts that shape how these systems work. Terms like agency, tool use, memory, and reasoning aren’t just buzzwords. They form the core of what makes today’s most capable AI agents intelligent and effective.
Why This Matters
Agentic AI isn’t just another step in the AI journey it’s a shift in how intelligence operates.
To make the most of it (or even just to follow the conversation), you need to understand how an agent “thinks” and acts.
So here are the essential terms, explained simply and clearly, to help you talk about and work with Agentic AI with confidence.
To make it concrete, we’ll use a simple example: lodging a car insurance claim after a minor accident.
Use Case: Filing a Car Insurance Claim
You’re in a small accident on your way home. Instead of calling customer service or filling in a long form, you just open your insurer’s app and say:
“Hi, I had a minor accident. Can I file a claim?”
Behind the scenes, an AI assistant kicks off the process gathering incident details, checking your policy, reviewing photos, estimating damage, and submitting the claim. No waiting, no back-and-forth.
Let’s break down what enables this intelligent, proactive experience.
Core Concepts: What Makes an Agent Work ?
1. Agent
An agent is an AI system that can sense its environment, make decisions, and take actions to achieve a goal.
In this case, your insurer’s digital assistant is the agent. It handles the entire claims process from capturing your accident report to submitting the final paperwork without needing a human claims officer at each step.
2. Agency
Agency is the agent’s ability to act on its own, interpreting your intent and deciding what to do next.
For example, when you say “I had a minor accident,” you don’t need to give step-by-step instructions. The agent immediately checks your coverage, identifies your location, and asks for photos all without being explicitly told.
3. Goal
A goal is the outcome the agent is trying to achieve.
In this case, the agent’s goal is to file your claim quickly and accurately. If you’ve been in a covered accident, its objective is to verify eligibility, estimate damage, and get the claim processed ideally without human delay.
4. Environment
The environment consists of all the external systems and data sources the agent can access.
For the claim, this includes your insurance policy database, repair shop availability, weather records (to verify conditions at the time of the accident), and even DMV records if needed. These help the agent make informed, real-time decisions.
5. Perception
Perception is how the agent interprets and understands incoming data whether it’s text, voice, or images.
For example, it reads your typed message (“Can I file a claim?”), analyzes uploaded photos to assess vehicle damage, and extracts timestamps and GPS data from images to verify the incident’s timing and location.
6. State
State refers to the internal memory the agent maintains throughout a task.
As you interact with it, the agent remembers that you’ve already uploaded the front-damage photo, shared your policy number, and submitted your GPS location. It doesn’t ask twice and knows what’s still pending like a copy of the police report.
How Agents Are Designed ? Architecture and Frameworks:
7. Architecture
Architecture is the system design that integrates all components: perception, planning, memory, tool use into a seamless workflow.
In the claim scenario, this architecture might involve:
· An image analysis module that detects damage from uploaded photos
· A policy lookup engine that checks your coverage
· A decision logic layer that evaluates the claim
· An API connector that submits the claim to backend systems
These components work together to create a unified agent experience, like a digital claims officer with specialized internal tools.
8. Planning
Planning is how the agent turns the goal (filing the claim) into a sequence of intelligent steps.
For example, the agent might follow this plan:
1. Parse the message: “I had a minor accident.”
2. Request photos and location data
3. Check the policy for coverage limits
4. Estimate repair cost using AI image analysis
5. Submit the claim and notify next steps
Instead of waiting for instructions, the agent actively sequences and executes these steps.
9. Orchestration
Orchestration is how tasks are distributed and coordinated across multiple systems or agents.
In this case, while one component verifies the insurance coverage, another might concurrently estimate repair time by querying a local repair shop’s availability via API. Both agents share progress in real time, so the overall process moves forward smoothly without delays.
10. Multi-Agent System
A multi-agent system involves several agents, each handling a specialised function, collaborating toward a common goal.
In a complex car accident claim, here’s how it might work:
· Agent A: Performs fraud detection by checking claim patterns and cross-referencing accident history
· Agent B: Communicates with the customer, gathers documents, and answers questions
· Agent C: Handles repair logistics by scheduling appointments with certified garages
Together, these agents form a coordinated digital workforce, ensuring faster and more accurate claims processing often without a human in the loop.
How the Agent Thinks ? Reasoning and Decision-Making:
11. Reasoning
Reasoning enables an agent to interpret complex inputs, resolve ambiguities, and make context-aware decisions.
For example, if a customer’s policy includes both general accident coverage and a clause excluding certain damage types, the agent evaluates both conditions, checks the specifics of the incident, and determines whether to approve the claim or escalate it for human review.
12. Chain of Thought (CoT)
Chain of Thought is a reasoning pattern where the agent breaks down a problem into logical steps before taking action.
In a claims scenario, the agent might follow this sequence:
· User uploads accident photos
· Image analysis confirms minor bumper damage
· Policy terms confirm coverage for low-impact incidents
· No previous similar claims detected
· Proceed to submit the claim automatically
This step-by-step reasoning ensures accuracy and transparency in decision-making.
13. LLM (Large Language Model)
An LLM is the core AI model that interprets natural language and generates meaningful responses.
When a user types “I bumped into another car, not a big deal,” the LLM understands the informal phrasing, extracts the intent (minor accident), and triggers the appropriate claims workflow — including data collection, policy matching, and damage estimation.
14. ReAct
ReAct (Reason + Act) is a loop in which the agent alternates between reasoning and taking actions based on real-time outcomes.
For instance, if the system detects that the user’s preferred repair center has no availability for the next 10 days, the agent evaluates proximity, turnaround time, and service quality — then recommends an alternative garage, adjusting its plan dynamically.
What the Agent Learns and Remembers ? Memory and Knowledge:
15. Memory
Memory allows an agent to retain context and preferences across interactions, enabling personalized experiences.
For example, if a customer previously opted for a specific repair shop or always requests a rental vehicle during repairs, the agent recalls those preferences and applies them automatically when processing a new claim — reducing friction and enhancing service continuity.
16. Memory Systems
Memory systems refer to the underlying infrastructure — databases, vector stores, or semantic indexes — that store and retrieve relevant historical data.
In an insurance context, this includes details like the customer’s claim history, communication logs, and interaction outcomes. When a returning user files a new claim, the system retrieves prior incident data to streamline the process and flag any repeat patterns that may indicate risk.
17. Knowledge Base
A knowledge base is a curated repository of structured information, such as policy documents, coverage rules, FAQs, and standard operating procedures.
If a user asks, “Does my comprehensive policy include roadside towing?”, the agent doesn’t guess — it searches the knowledge base, locates the relevant clause, and delivers a policy-specific, accurate response grounded in official documentation.
How the Agent Gets Things Done ? Tools and Execution:
18. Tool Use
Tool use refers to the agent’s ability to interact with external systems — APIs, services, or databases — to perform actions beyond language generation.
In an insurance scenario, this could include calling a damage assessment API to estimate repair costs, scheduling a vehicle inspection with a local garage, or automatically updating the internal claims system with status logs and customer details.
19. RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is a method where the agent retrieves relevant, real-time information from external sources before formulating a response.
For example, when a customer asks, “How long does it usually take to get my car back from repairs?”, the agent queries the repair network’s latest job queue data and returns a current, location-specific estimate — instead of relying on outdated or generic information.
20. Prompt Engineering
Prompt engineering is the practice of designing precise, structured inputs that guide the AI to generate high-quality, reliable outputs.
In practice, a well-crafted prompt like:
“Evaluate this claim for minor vehicle damage. If policy ID covers it, return a status update and next steps using no more than three sentences.”
ensures the LLM interprets the task accurately, responds concisely, and aligns with compliance and customer communication standards.
Recap: A Smarter, Faster Claim Experience
With Agentic AI, a simple message like “I had a minor accident” isn’t just a trigger for a chatbot to respond, it’s the start of a complete, intelligent process that unfolds on your behalf. It initiates a series of steps that, traditionally, would involve multiple systems, departments, and human effort, all coordinated seamlessly by an AI agent working behind the scenes.
This AI agent does far more than just respond with links or collect details. It:
Understands your intent using a combination of language models (LLMs), structured prompts, and real-time perception. It knows you’re reporting a claim, not just chatting, and picks up the context like urgency, damage, and tone without needing exact instructions.
Makes informed decisions and takes real action. It reasons through your request: does the policy apply? Is the damage covered? What steps are needed? Then it plans the next actions collecting images, verifying coverage, assessing costs, and even submitting the claim all automatically, with no need for human hand-holding.
Remembers your preferences and past interactions using state, memory systems, and knowledge bases. It recalls that you declined a rental car in your last claim, that your policy has an excess waiver, and that you prefer email updates. It tailors its actions based on this continuity giving you a smooth, personalized experience.
Interacts dynamically with real-world systems. Through APIs, RAG (Retrieval-Augmented Generation), and orchestration, the agent fetches real-time repair estimates, books inspection appointments, updates your claim in the CRM, and communicates with third-party repairers all in the background.
This is a big shift from traditional automation, which relies on static rules and manual triggers. Agentic AI introduces a new way of working where software behaves more like a smart assistant than a tool, handling tasks proactively, adapting to your needs, and improving with each interaction.
The result?
Less paperwork; Fewer delays; Better decisions; Happier customers
By turning vague user inputs into structured, goal-driven action, Agentic AI is transforming what’s possible in claims and across every customer-facing function in insurance and beyond.
Conclusion
These 20 terms aren’t just technical jargon they’re the building blocks of how modern AI agents work.
If you’re working in insurance, tech, or operations, knowing these concepts helps you understand what’s really going on inside these AI-driven systems.
You’ll be better equipped to adopt, design, or evaluate intelligent agents that move beyond automation and into meaningful decision-making and action.
Stay tuned for the next part…
