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

Context windows are the amount of text an AI agent can “see” at one time when generating a response. They define how much prior conversation, instructions, retrieved data, and tool output the model can use before older information falls out of view.

How It Works

Think of a context window like a teller’s working memory during a customer interaction.

A teller can only keep so much in mind at once: the customer’s request, their account type, a recent transaction, a policy note, and maybe one or two exceptions. If the conversation gets long, earlier details get forgotten unless they were written down somewhere visible.

An AI agent works the same way. It does not remember everything forever inside the model. Instead, it receives a limited block of text each time it responds:

• •system instructions
• •user messages
• •previous assistant replies
• •tool results
• •retrieved documents or account data

If all of that fits inside the window, the agent can reason over it together. If not, older content gets dropped or summarized.

For product managers in retail banking, this matters because an AI agent is often juggling:

• •customer intent
• •compliance instructions
• •product eligibility rules
• •account history
• •fraud signals
• •knowledge base articles

The key design question is not “Can the model answer?” It is “What information must be in scope right now for the answer to be safe and useful?”

A simple analogy: imagine a banker preparing for a call with only one sheet of paper on their desk. That sheet can hold the customer’s name, last issue, and current offer. If the case is complex, they need either a bigger sheet or a way to swap in new notes as the conversation progresses.

That is what context management does in AI agents:

• •keeps critical facts in view
• •summarizes older turns
• •retrieves relevant records on demand
• •removes noise that would waste space

For engineers, context window size is measured in tokens, not words. Tokens are chunks of text; depending on language and formatting, 1 token is roughly 3/4 of a word in English. A 128k-token window sounds huge, but it fills up fast once you add long policies, chat history, and document excerpts.

Why It Matters

• •

  It affects answer quality

  • •If key policy details fall out of context, the agent may give incomplete or wrong guidance.
  • •In banking, that can mean bad product recommendations or incorrect fee explanations.
• •

  It affects compliance

  • •The agent needs current instructions visible when handling regulated topics.
  • •If an outdated rule drops out of context, you risk inconsistent behavior across conversations.
• •

  It affects customer experience

  • •Long support journeys often span multiple turns.
  • •A small context window can make the agent forget what the customer already said and force repetition.
• •

  It affects cost and latency

  • •Larger contexts usually mean more compute per request.
  • •More tokens also increase response time and infrastructure spend.

Here is the practical PM takeaway:

Real Example

A retail bank launches an AI agent to help customers dispute card transactions.

The flow looks simple at first:

1. •Customer says they do not recognize a charge.
2. •Agent asks for transaction date and merchant name.
3. •Agent checks dispute eligibility rules.
4. •Agent starts the dispute process if criteria are met.

Now add real-world complexity:

• •The customer has already chatted with another bot earlier.
• •The dispute policy differs for debit vs credit cards.
• •Some transactions require fraud escalation instead of standard dispute handling.
• •The customer mentions they recently traveled abroad.
• •The bank has a rule that certain merchants are excluded from automatic disputes.

If all of that fits in context, the agent can make a better decision.

If not, here is what happens:

• •it may miss that the card was debit rather than credit
• •it may ask for information already provided
• •it may apply the wrong dispute path
• •it may fail to surface an escalation warning

A production-grade setup usually solves this by combining three things:

• •Short conversation history: only recent turns stay in the prompt
• •Retrieval: pull relevant policy snippets or account facts when needed
• •Summarization: compress older parts of the conversation into a compact state

Example state summary:

Customer: Maria Lopez
Issue: Unrecognized $84.20 charge on debit card ending 4421
Already confirmed: transaction date = May 14; merchant = "NOVA TRAVEL"
Risk flags: recent international travel; no prior disputes this quarter
Next step: check debit card dispute eligibility and fraud escalation criteria

That summary uses far less space than replaying every message from the whole chat. More importantly, it preserves exactly what matters for decision-making.

Related Concepts

• •

  Tokens
  The unit models use to measure text length. Context windows are token-based limits.

• •

  Prompt engineering
  How you structure instructions and inputs so the model uses its context well.

• •

  Retrieval-Augmented Generation (RAG)
  A pattern where external documents are fetched into context at response time.

• •

  Conversation memory
  Techniques for storing useful facts outside the raw chat history so agents don’t forget them.

• •

  Summarization pipelines
  Methods for compressing long interactions into shorter state summaries without losing critical details.

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.