Context Engineering in 2026: Compaction, Memory & Cost

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Official Description

Every long agent session eventually breaks: the assistant that swore it would "never push to main"

does exactly that forty turns later. The model didn't get dumber — its context did. This workshop is

about engineering the context window so that stops happening, shown with Towards AI's open-source AI

tutor, which answers questions for students of our AI-engineering courses. Context engineering is

deciding what the model sees on every single call — instructions, history, retrieved course content,

memory, and tool outputs — and it's the line between a tutor that holds a coherent session and one

that forgets the student's setup halfway through. We'll move in three stages, mirroring how the

project actually went. The concepts: the two root problems (a finite window, a stateless model), the

full compaction toolkit (truncation, trimming, tool-result clearing, summarization, and offloading

to files — and when each actually helps), memory that survives across sessions, skills loaded on

demand, and production-grade retrieval (chunking, metadata, course scoping, hybrid search,

reranking, and evaluating). We'll cover the tutor's architecture, and the evaluation harness we used

to measure every run on Gemini — tokens, cost, latency, and memory probes instead of vibe-checks. At

real volume, even Gemini Flash got expensive, so we tested whether open and local models could match

the quality for a fraction of the cost and match result quality. Everything is open-source and will

be shared during the workshop.

Related YouTube Video

Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

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