Stop babysitting your agents... — Brandon Waselnuk, Unblocked

2026-05-26 · Source: AI Engineer · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, long

Summary

Unblocked introduces a "context engine" designed to eliminate the need for engineers to "babysit" AI agents by providing comprehensive, dynamic organizational context. The presentation debunks three myths: naive RAG over documentation, merely connecting multiple communication protocols (MCPs), and relying solely on large context windows are insufficient for effective agent operation. Instead, a robust context engine integrates static corporate knowledge with real-time runtime data, reasoning across diverse data sources to deliver token-optimized, accurate responses. Key components include a social graph for personalized relevance, conflict resolution mechanisms, and robust data governance. This approach enables agents to produce high-quality, mergeable code, significantly improving accuracy and efficiency, as demonstrated by a comparison where an agent with the engine produced merge-ready code versus a naive run that would have broken the system. An open-source social graph tool is also highlighted.

Key takeaway

For AI Engineers and teams struggling with unreliable AI agents or high operational costs, implementing a dedicated context engine is critical. This approach moves beyond basic RAG or large context windows, enabling agents to access and reason over dynamic, personalized organizational knowledge. Your agents will generate high-quality, mergeable code and automate complex tasks, significantly reducing manual oversight and token expenditure, transforming them into effective, autonomous team members.

Key insights

AI agents require dynamic, comprehensive organizational context, not just intelligence or access, to operate effectively and produce mergeable outputs.

Principles

• AI agent performance depends on context, not just intelligence.
• Context engines must resolve conflicting information for truthiness.
• Personalized context, like social graphs, enhances agent relevance.

Method

A context engine ingests diverse data (static, runtime, SaaS), reasons across it using social graphs and conflict resolution, then delivers token-optimized, permission-aware context to agents for execution.

In practice

• Build social graphs to personalize agent context for individual engineers.
• Integrate conflict resolution to reconcile disparate corporate data sources.
• Deploy context engines for automated engineering support and code generation.

Topics

• AI Agents
• Context Management
• Knowledge Graphs
• Retrieval-Augmented Generation
• Token Optimization
• Software Engineering

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

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.
• Context Is the New Code — Patrick Debois, Tessl — Effective AI agent performance hinges on meticulously engineered context, necessitating a dedicated Context Development Life Cycle.
• AI Dev 26 x SF | Brandon Waselnuk: Building the Context Engine AI Agents Need — AI agents require a dynamic context engine to achieve organizational intent and avoid costly errors.
• What Is a Context Graph — and Why Is Everyone Talking About It? — Context graphs provide machine-readable institutional memory, enabling AI to accurately interpret complex organizational knowledge and decision logic.
• 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.
• 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.

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