What is context windows in AI Agents? A Guide for product managers in insurance

A context window is the amount of information an AI agent can hold and use at one time while generating a response or taking an action. In practice, it is the agent’s short-term working memory: everything it can “see” from the conversation, documents, tool outputs, and instructions before older information starts falling out.

How It Works

Think of a context window like a claims adjuster’s desk during a busy day.

The adjuster can only keep so many files open at once. They may have the policy document, the claim form, the customer’s notes, and a photo of the damage in front of them. If a new file comes in and the desk is full, something has to be put away before they can work with the new one.

AI agents work the same way.

When an insurance agent receives a user request, it builds a prompt from:

• •The system instructions
• •The current conversation
• •Retrieved policy or product documents
• •Tool results like claim status or underwriting data
• •Any structured memory the application adds

All of that has to fit inside the model’s context window. If it does not, older content gets truncated or summarized.

For product managers, this matters because an AI agent is not “remembering” everything forever. It is only reasoning over what fits in its current window. That means the quality of the answer depends on what you place into that window, in what order, and how much room you leave for the model’s response.

A useful analogy is email thread history.

If you reply to a long chain with too much back-and-forth, people stop reading earlier messages and focus on the latest relevant parts. The same thing happens with context windows: recent and relevant content usually matters more than old noise.

Why It Matters

• •

  It affects answer quality

  • •If key policy details fall out of the window, the agent may give incomplete or wrong guidance.
  • •This is especially risky in insurance where coverage terms and exclusions are precise.
• •

  It drives cost and latency

  • •Bigger context windows usually mean more tokens processed per request.
  • •More tokens can increase API cost and slow down response times.
• •

  It shapes product design

  • •You cannot just keep appending every chat turn forever.
  • •You need strategies for summarization, retrieval, and pruning so the agent stays useful over long interactions.
• •

  It impacts compliance and auditability

  • •In regulated workflows, you need to know which facts were actually visible to the model when it made a decision.
  • •That matters for explainability in claims triage, underwriting support, and customer service.

Real Example

Consider a motor insurance claims assistant helping a customer after an accident.

The customer says:

• •“I was rear-ended yesterday.”
• •“My policy number is X123.”
• •“I already uploaded photos.”
• •“Can I get towing covered?”

The agent pulls in:

• •Policy summary
• •Coverage rules
• •Recent claim history
• •Photo metadata
• •Towing benefit details

Now imagine this interaction continues for 25 turns because the customer asks about excess, rental car limits, repair network options, and whether a second driver is covered. If every turn plus every retrieved document is kept verbatim, the context window fills up fast.

At that point, one of two things happens:

1. •The system drops older content automatically.
2. •The platform summarizes earlier turns into compressed notes.

If done well, the agent still knows:

• •The claim is for rear-end collision
• •Towing may be covered under roadside assistance
• •Rental car entitlement depends on policy tier

If done poorly, it may lose:

• •Which vehicle was involved
• •Whether towing was already approved
• •A prior statement that changes coverage eligibility

For a product manager, this means your design should not assume endless memory. Instead:

• •Keep critical facts in structured fields outside chat history
• •Retrieve only relevant policy clauses when needed
• •Summarize long conversations into durable case notes
• •Limit unnecessary chit-chat that burns context budget

Here is what that looks like architecturally:

User chat -> intent detection -> retrieve relevant policy snippets -> add structured case state -> call model -> write back summary to CRM/case record

That pattern keeps the model focused on current facts instead of forcing everything through one giant prompt.

Related Concepts

• •

  Tokens

  • •The units models count to measure input and output size.
  • •Context windows are usually described in token limits.
• •

  Prompt engineering

  • •How you structure instructions and input inside the available window.
  • •Good prompt design helps models use limited space better.
• •

  Retrieval-Augmented Generation (RAG)

  • •Pulling external documents into context only when needed.
  • •Useful when policy libraries are too large to fit in full.
• •

  Memory

  • •Persistent storage outside the model’s temporary working memory.
  • •Often used for customer preferences, case status, or prior decisions.
• •

  Summarization

  • •Compressing long conversations into shorter notes.
  • •Commonly used to preserve important facts when context gets crowded.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.