The Data Agent Stack - Part 3: Context Assembly for Data Agents

2026-02-17 · Source: The Agent Stack · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, long

Summary

The Data Agent Stack - Part 3" details the critical process of context assembly for data agents, emphasizing that a model reasons over a bounded "evidence bundle" rather than an entire data platform. This process involves resolving the user's question, generating broad candidate evidence from diverse sources like metric contracts, documents, and live checks, and then rigorously filtering and ranking this evidence based on permissions, authority, freshness, and scope. The article differentiates context assembly from simpler RAG systems by including governance over conflicts, placement, and reconstructability. It outlines how prepared evidence provides speed while live verification ensures currency, and stresses the importance of a context budget to avoid over-retrieval and attention dilution. Finally, it introduces the "context manifest" as a crucial artifact for reproducibility and debugging, detailing common failure modes and a builder checklist for robust implementation.

Key takeaway

For AI Engineers or MLOps Engineers building data agents, recognize that model reliability depends on meticulously constructing the "evidence bundle", not just generating SQL. You must implement robust context assembly systems that resolve questions, apply permission-filtered, authority-based retrieval, and manage conflicts across diverse sources. Prioritize a context budget and persist a context manifest to ensure reproducibility and debug divergent answers, preventing common failures like over-retrieval or stale context.

Key insights

The reliability of data agents hinges on constructing a permission-scoped, authoritative, and bounded "evidence bundle" for each query.

Principles

• Models reason over constructed evidence.
• Authority governs evidence selection.
• Explicit conflict resolution is vital.

Method

Context assembly involves question resolution, broad candidate generation, permission-filtered retrieval, authority-based ranking, conflict resolution, and budgeting for prepared and live evidence.

In practice

• Inventory all context sources.
• Define source-specific retrieval.
• Assign authority by claim type.

Topics

• Data Agents
• Context Assembly
• RAG Systems
• Data Governance
• Evidence Management
• Context Manifests

Best for: AI Engineer, MLOps Engineer, AI Architect

Related on AIssential

• The Agent Stack - Part 5: Context, Retrieval, and Memory — Context is the runtime-assembled working set, not the model's inherent knowledge.
• How OpenAI Built Its Data Agent — The reliability of AI agents at scale stems from strong data foundations and carefully engineered context, not complex agent architectures.
• Your RAG System Retrieves the Right Data — But Still Produces Wrong Answers. Here’s Why (and How to Fix It). — RAG systems can confidently provide wrong answers when conflicting information is silently resolved during context assembly.
• Building Reliable Agentic AI Systems — Agentic RAG systems require robust context and harness engineering for reliability and trust in regulated environments.
• Context Engineering 2.0: MCP, Agentic RAG & Memory // Simba Khadder — Unifying RAG, memory, and structured data into a single "context engine" is crucial for effective AI agents.
• Your AI agents are operating on 15% of the information they need — Enterprise AI agents are hampered by context window inefficiencies and probabilistic RAG, requiring an architectural shift to data-layer intelligence for reliable reasoning.

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Agent Stack.