How to build AI product sense

2025-05-13 · Source: Lenny's Newsletter · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

LangChain and Manis co-hosted a webinar on "Context Engineering," a discipline focused on managing the context window of Large Language Models (LLMs) in agentic systems. Lance, a founding engineer at LangChain, provided an overview, noting that context engineering emerged around May 2023, driven by the "year of agents" and the challenge of unbounded context growth from tool calls. He highlighted common themes: context offloading (e.g., to file systems), context reduction (summarization/compaction), context retrieval (semantic search or simple file search), and context isolation (multi-agent setups). Pete, co-founder and chief scientist of Manis, then shared fresh lessons, emphasizing that context engineering is a practical boundary between application and model. He detailed Manis's approach to context reduction via reversible compaction and careful summarization, context isolation through shared memory or communication, and a novel layered action space for offloading tools, comprising atomic function calls, sandbox utilities, and external packages/APIs.

Key takeaway

For AI Architects and NLP Engineers building agentic systems, you should prioritize context engineering as a critical boundary between your application and the underlying LLM. Focus on simplifying the model's task by implementing reversible context compaction and structured summarization, while leveraging layered action spaces to offload tools and manage context growth effectively. Regularly re-evaluate your architecture against evolving model capabilities to remove unnecessary complexity and ensure optimal performance and cost efficiency.

Key insights

Context engineering optimizes LLM agent performance by managing context growth through offloading, reduction, isolation, and layered action spaces.

Principles

• Context engineering simplifies the model's job, not complicates it.
• Compaction is reversible; summarization is not.
• Prioritize line-based data formats for easier processing.

Method

Manis employs a layered action space for tool offloading, using atomic function calls, sandbox utilities for pre-installed commands, and Python scripts for external APIs, all routed through standard function calls.

In practice

• Use structured schemas for summarization to ensure key information is retained.
• Offload token-heavy tool outputs to a file system, returning only a reference.
• Employ sub-agents as tools for complex, multi-query tasks with fixed output schemas.

Topics

• Context Engineering
• AI Agents
• Context Management
• Multi-Agent Systems
• Tool Calling

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

Related on AIssential

• Context Engineering for AI Agents: Part 2 — Effective AI agent performance hinges on precise context management, minimizing irrelevant information and optimizing tool access.
• I Built an AI Data Engineering Mentor from Scratch: Here’s What Nobody Tells You — AI agent effectiveness is primarily driven by structured context, not just the underlying large language model.
• Why Bigger Context Windows Make AI Worse — Large context windows degrade LLM performance and increase costs; effective context compaction is crucial for agent reliability.
• Context Engineering Explained in 3 Levels of Difficulty — Context engineering manages LLM context windows as dynamic resources to prevent information loss and performance degradation.
• The importance of Agent Harness in 2026 — Agent Harnesses are essential infrastructure for managing reliable, long-running AI agent tasks, moving beyond model-centric development.
• Context Is the New Code — Patrick Debois, Tessl — Effective AI agent performance hinges on meticulously engineered context, necessitating a dedicated Context Development Life Cycle.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Lenny's Newsletter.