AI Dev 26 x SF | Brandon Waselnuk: Building the Context Engine AI Agents Need

2026-05-22 · Source: DeepLearningAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, extended

Summary

Brandon Waselnuk's presentation, "Building the Context Engine AI Agents Need," highlights the critical role of dynamic context for AI agents to perform effectively within an organization. He argues that agents, like new hires, initially lack crucial company-specific understanding, leading to inefficient or incorrect outputs. The talk identifies three common AI adoption stallouts: reliance on stale curated context, the "satisfaction of search" from merely connecting multiple data sources, and the inability to provide dynamic context at runtime. Waselnuk debunks four myths, including that naive RAG or larger context windows are sufficient. He details that a true context engine must understand user roles, resolve data conflicts, respect permissions, and deliver tightly scoped, token-optimized information. Unblocked AI's approach involves ingesting diverse data, building knowledge graphs, and offering tools like a Social Graph Builder and Repo Rules Agent, demonstrating up to 80% faster task completion and 50% token cost reduction in production.

Key takeaway

For MLOps Engineers deploying AI agents, recognize that merely providing data access is insufficient; agents need a dynamic context engine to achieve reliable, intent-aligned outcomes. Your current manual context provision is a bottleneck. Implement a system that unifies organizational knowledge, resolves data conflicts, and delivers token-optimized context to avoid costly "doom loops" and significantly reduce token spend and human correction time. Explore open-source tools like social graph builders to enhance agent understanding.

Key insights

AI agents require a dynamic context engine to achieve organizational intent and avoid costly errors.

Principles

• Access to information does not equal understanding.
• Bad context costs compound with agent autonomy.
• Context engines must resolve data conflicts dynamically.

Method

The proposed method involves ingesting diverse organizational data via APIs, building embeddings and knowledge graphs, and then using these to provide token-optimized, personalized, and conflict-resolved context to agents at runtime.

In practice

• Use a social graph builder to identify team experts.
• Integrate context engines into CI runners for bug validation.

Topics

• AI Agents
• Context Engines
• Knowledge Graphs
• Retrieval-Augmented Generation
• MLOps
• Code Review Automation

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked — Context engines optimize AI agent performance by providing highly relevant, curated organizational context, moving beyond simple data access.
• Stop babysitting your agents... — Brandon Waselnuk, Unblocked — AI agents require dynamic, comprehensive organizational context, not just intelligence or access, to operate effectively and produce mergeable outputs.
• Why Enterprise AI Fails Without a Context Engine - with Eran Yahav of Tabnine — An enterprise context engine provides AI agents with crucial organizational knowledge, significantly boosting success rates and efficiency.
• Build an AI knowledge fabric for your organization — An AI knowledge fabric provides structured, dynamic context for autonomous agents, preventing hallucinations and improving efficiency.
• Building Reliable Agentic AI Systems — Agentic RAG systems require robust context and harness engineering for reliability and trust in regulated environments.
• AI agents are learning on the job — just not for your whole team — Enterprise AI agents lack shared memory, hindering team productivity; shared context is vital for multi-user workflows.

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Editorial summary, takeaway, and curation by AIssential. Original article published by DeepLearningAI.