What is context windows in AI Agents? A Guide for developers in lending

Context windows are the amount of text, tool output, and conversation history an AI agent can actively keep in mind at one time. In practice, a context window is the maximum input size the model can read before it starts forgetting earlier parts of the conversation or truncating them.

How It Works

Think of a context window like a loan officer’s desk during underwriting.

The desk can only hold so many documents at once: application form, bank statements, payslips, ID, credit report, maybe a few notes from the last call. If you keep adding more files, older ones get pushed aside unless someone deliberately archives them elsewhere.

An AI agent works the same way.

• •The model receives a prompt plus prior messages plus tool outputs.
• •All of that is converted into tokens, not words.
• •The total token count must stay within the model’s limit.
• •Once you exceed the limit, something gets cut off:
  • •older chat turns
  • •long retrieved documents
  • •tool results
  • •system instructions, if your orchestration is bad enough

For lending teams, this matters because agent workflows are rarely short. A borrower asks about eligibility, uploads payslips, the agent calls a KYC service, checks affordability rules, summarizes exceptions, and drafts a response. Every one of those steps consumes context.

A simple way to think about it:

The key engineering point: the model does not “remember” like a database. It only sees what fits in its current window. If your agent needs information later, you must either re-inject it or retrieve it from external storage.

Why It Matters

Developers in lending should care because context windows affect both product quality and compliance.

• •

  Long customer journeys break easily

  • •Loan applications often involve multi-step conversations.
  • •If the agent loses earlier eligibility details, it may repeat questions or give inconsistent answers.
• •

  Important facts can disappear

  • •Income figures, employment type, collateral details, and exception notes are easy to lose when the conversation gets long.
  • •That creates bad decisions and poor customer experience.
• •

  Compliance depends on what the model can see

  • •If policy text or required disclaimers fall out of context, the agent may answer incorrectly.
  • •In regulated workflows, that is not just a UX bug; it is an audit issue.
• •

  Costs rise with bigger prompts

  • •Larger context windows usually mean more tokens per request.
  • •More tokens means higher latency and higher inference cost across high-volume lending operations.

Real Example

Say you are building an AI assistant for mortgage pre-qualification.

The flow looks like this:

1. •Customer asks whether they qualify for a £250k mortgage.
2. •Agent asks for income, deposit amount, employment status, and monthly debts.
3. •Agent calls tools:
   • •credit bureau lookup
   • •affordability calculator
   • •product eligibility rules
4. •Agent summarizes results and explains next steps.

Now imagine the customer has been chatting for 20 minutes. They uploaded payslips early on, then asked about fixed vs variable rates, then came back with a question about debt-to-income ratios.

If your agent only keeps the last few turns in context:

• •it may forget their salary
• •it may lose the deposit amount
• •it may miss that they are self-employed
• •it may give a generic answer instead of a tailored one

A production-grade design would handle this by splitting responsibilities:

• •

  Short-term context

  • •Keep only the active conversation and current task in the model window.
• •

  Structured state

  • •Store key facts separately:
    • •applicant name
    • •income
    • •deposit
    • •property value
    • •affordability result
    • •decision status
• •

  Retrieval

  • •Pull policy snippets or product rules only when needed.

Here is what that looks like in practice:

{
  "applicant_id": "A12345",
  "income_monthly": 6200,
  "deposit": 40000,
  "employment_status": "self_employed",
  "debt_monthly": 850,
  "affordability_result": {
    "max_loan": 238000,
    "decision": "pre_qualified"
  }
}

When the customer later asks, “Why did I only qualify for £238k?”, you do not rely on old chat history alone. You inject the stored state plus the relevant affordability rule into the prompt. That keeps answers consistent even if the original conversation was dozens of turns ago.

This is how serious lending agents should be built: not by hoping everything stays inside one giant prompt, but by treating context as a managed resource.

Related Concepts

• •

  Tokens

  • •The unit models use to measure input size.
  • •Important for estimating prompt length and cost.
• •

  Prompt engineering

  • •How you structure instructions so the model uses its limited context effectively.
• •

  Retrieval-Augmented Generation (RAG)

  • •Pulling policy docs or knowledge base content into context only when needed.
• •

  Conversation memory

  • •Storing user facts outside the window so they can be reloaded later.
• •

  Tool calling / function calling

  • •Letting agents query systems like LOS platforms, CRMs, or credit decision engines instead of stuffing everything into prompt text.

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.