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World Congress 2026 Europe • Jul 10, 2026 • Session details

Context is all you need

Raphael De Lio , Samuel Agbede

Are massive context windows tanking your AI's performance? Discover why building a real-time context engine is the actual secret behind hyper-contextualized, self-learning enterprise agents.

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#1 about 3 min

Transitioning from simple prompts to complex agent task execution

Modern language models have evolved from basic factual retrieval to executing complex software engineering workflows like server deployment.

#2 about 1 min

Identifying fragmented context as the primary bottleneck for agents

High tool-calling accuracy reveals that providing the correct data state is the actual rate-limiting factor for autonomous implementations.

#3 about 2 min

Performance limitations of million-token context windows in models

Massive context architectures suffer from the lost in the middle phenomenon where scattered relevant data degrades retrieval performance.

#4 about 1 min

Managing latency and operational costs in transformer attention layers

Processing large contexts exponentially increases matrix operations leading to severe latency and cost bottlenecks.

#5 about 3 min

Designing a real-time context strategy for enterprise workflows

Connecting scattered business data to autonomous agents requires systems configured for rapid retrieval and dynamic navigation boundries.

#6 about 2 min

Optimizing retrieval layers with vector and full-text search

Combining semantic recall with full-text precision limits false positives during dynamic context retrieval.

#7 about 2 min

Building cross-session memory as a product integration moat

Persisting user preferences, changing goals, and historical actions across sessions inherently differentiates competitive artificial intelligence platforms.

#8 about 3 min

Implementing asynchronous memory extraction and lifecycle management pipelines

Isolating short-term context through queued background workers ensures durable facts are safely indexed into long-term databases.

#9 about 3 min

Demonstrating continuous database updates using real-time memory extraction

Real-time background extraction automatically updates schema states without interrupting the current interactive application session.

#10 about 4 min

Creating self-learning agents from historical execution traces

Analyzing prior interactions allows automated systems to discover navigational shortcuts and optimize future execution paths autonomously.

#11 about 3 min

Balancing retrieval precision with context window summarization tradeoffs

Retrieving narrow file scopes instead of full contexts minimizes lossy summarization limits and preserves actionable data fidelity.

Matching moments

1:15 min

Equipping AI agents with memory and context

Oren Penso Oren Penso · WWC Europe 2026

2:21 min

Applying context engineering across the full software lifecycle

Neel Sundaresan Neel Sundaresan +1 · WWC Europe 2026

1:24 min

Designing short-term and long-term memory for intelligent agents

Zaid Zaim Zaid Zaim +1 · WWC Europe 2026

1:12 min

Context-aware AI for enterprise business applications

Daniel Oh Daniel Oh · WWC Europe 2026

50 sec

Writing context-dense frontend code for AI agents

Carl Assmann Carl Assmann · WWC Europe 2026

1:51 min

Modernizing interconnected enterprise systems with context-aware AI

Alex Laubscher Alex Laubscher · WWC 2025

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