Context Engineering for Agentic Hybrid Applications

Research survey and upcoming trends discussion - Ivan Potapov and Tobias Lindenbauer
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​As an agent keeps running, its context window balloons with tool logs, stale diffs, and repeated data dumps. The model starts drowning in irrelevant details and falls victim to "lost-in-the-middle" effects — missing critical facts buried deep in oversized prompts.

​We'll walk through research for keeping only high-signal observations: masking vs. summarization trade-offs, compressing bulky tool output (drawing from ideas like LLMLingua-2), and pruning dead branches from the agent's trajectory so it stops dragging noise forward. We'll also share insights on cutting LLM call costs along the way.

​Then we'll connect those techniques to bigger-picture design: memory hierarchies (session → working set → notes → cross-session) and standardized tool interfaces like MCP that reduce "context debt" and keep the agent's working set clean.

​Finally, we'll look at where the field is heading — toward a world where Context Engineering becomes something you train, not just script.
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​About the Speakers:
​Tobias Lindenbauer is an AI researcher at JetBrains Research, where he advances efficient and effective code agents that robustly solve long-horizon software engineering tasks. Currently, he is most interested in efficiency topics, context management, interpretability and data synthesis. He recently presented “The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management” at the Deep Learning for Code workshop at NeurIPS 2025, highlighting practical pitfalls of LLM summarization-based context strategies and evidence for more computationally efficient alternatives.
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​Ivan Potapov is a Research Engineer in Discovery Search & Ranking at Zalando, where he builds search retrieval and ranking systems. He teaches data engineering, AI agents, and LLM alignment, with a focus on bridging software engineering and applied ML. His recent work centers on long-running agents and context engineering—memory, state, and retrieval—exploring why many code-first agent designs fall short. His key thesis: context management is becoming something we train and iterate on, not just script. https://blog.ivan.digital/context-engineering-for-agentic-hybrid-applications-why-code-agents-fail-and-how-to-fix-them-076cab699262
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