Optimize Your AI Agents’ Context Window: Multi-Query and Temporal Memory with Claix

The Problem: Agent Amnesia and the Cost of Reprocessing

Autonomous agents rarely solve complex problems in a single step. They follow an iterative reasoning loop (Chain of Thought): first they need to know what a contract is about, then they hunt for penalty clauses, then they check whether dates line up.

In a traditional pipeline the agent hits a technical dilemma:

• Context loss: stuffing the raw PDF into every prompt saturates the model’s context window (Lost in the Middle / attention drop) and inflates OpenAI or Anthropic bills.
• Extreme latency: re-running OCR or the PDF parser for every new question the agent invents is not viable in production.

Claix’s Multi-Query Architecture

Claix splits ingestion from querying and treats your database as an extension of the agent’s RAM:

Step 1: One-time parsing

The agent sends the file (PDF, Excel, HTML). Claix converts it to an optimal format (Markdown/TSV) and stores it with an expires_at TTL (up to 24 hours, configured on the schema).

Step 2: Multi-querying

While the document is alive in Claix’s context window, the agent can send question arrays (up to 5 per call) to POST /window-context/{document_id}. The backend injects only the clean text into the internal LLM for that specific doubt. If a fact is not in the document, that answer is null — no forced hallucination.

Step 3: Auto-destruction

When the TTL expires, a cron job purges the document. Zero long-term storage residue. You stay GDPR-aligned without standing up buckets or vector indexes just for a reasoning session.

┌──────────────────┐     ┌─────────────────────────────┐     ┌──────────────────────────┐
│  One-time ingest │ ──► │  Markdown/TSV + TTL (up to  │ ──► │  Multi-query (up to 5    │
│  PDF/Excel/HTML  │     │  24 h) in documents         │     │  questions / call)       │
└──────────────────┘     └─────────────────────────────┘     └──────────────────────────┘
                                      │                                  │
                                      ▼                                  ▼
                             expires_at / Cron Job              Millisecond answers
                             Auto-destruction                   without re-parsing the file

Comparison: Traditional Extraction vs. Claix Context Window

Use Cases: Why Agents Need to Ask More Than Once

Dynamic audits

A finance agent reviews a balance sheet (Excel). First it asks for net profit. If profit is negative, it sends a second Claix request for the operating-expense breakdown — without parsing the workbook again.

Customer-support triage

An agent receives a giant email thread (HTML). The first Claix call extracts the customer ID. It looks that ID up in an internal database, then asks Claix again for the complaint messages tied to that ID.

The context window turns Claix from a simple PDF extractor into short-term working memory that AI agents actually need.

How to call it from your agent

Enable window_context on the schema (window_time up to 1440 minutes). Extraction returns a document_id. Until it expires, POST https://www.claix.dev/window-context/{document_id} with a questions array of up to 5 strings. The same capability is exposed as the MCP tool claix.window_context.ask.