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# Context is all you need

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.

- **Speakers:** [Raphael De Lio](https://www.wearedevelopers.com/@raphael-de-lio), [Samuel Agbede](https://www.wearedevelopers.com/@samuel-agbede)
- **Event:** World Congress 2026 Europe
- **Published:** July 10, 2026
- **Duration:** 24:18
- **URL:** https://www.wearedevelopers.com/videos/100294-context-is-all-you-need

## Summary

The shift in modern artificial intelligence development has moved from maximizing model reasoning to mastering context quality. Although large language models have vastly improved their tool-calling accuracy, many production agents still fail because their context is fragmented, stale, or difficult to navigate. Relying solely on massive, million-token context windows is an ineffective solution; LLMs suffer from the lost in the middle phenomenon where mid-prompt data degrades model performance, and the quadratic attention costs of transformer architectures lead to high latency and ballooning expenses. Instead, building a real-time context engine is critical for optimizing agent behavior. This requires a dedicated context engineering strategy that connects scattered data through precise retrievers, moving beyond simple vector search which often yields high recall but low precision to embrace robust full-text and hybrid search methodologies. A mature context strategy ensures agents can dynamically navigate to the most relevant information while keeping the default context window as minimal as possible to avoid prompt crowding. Furthermore, cross-session memory is rapidly becoming a competitive moat for enterprise integrations. By managing both short-term conversational context and long-term durable facts using tools like Redis, agents can personalize user experiences seamlessly. A standout application of this architecture is the development of self-learning agents. By logging previous interaction traces and using asynchronous background workers to extract behavioral lessons, agents can actively optimize their future workflows. For example, capturing a browser agent's navigation paths allows the model to learn from past inefficiencies, cutting processing time and boosting performance without blocking real-time interaction. Ultimately, integrating scalable short-term states with highly accessible long-term memory separates generic commodity models from intelligent, hyper-contextualized enterprise systems.

**Keywords:** context engineering, real-time context engine, AI agent memory, model context protocol, lost in the middle phenomenon, hybrid search integration, LLM attention costs, self-learning AI agents, long-term durable facts, automated agent tracing, short-term agent state, transformer architecture latency, dynamic context extraction, redis database caching, cross-session user preferences

## Chapters

1. **Transitioning from simple prompts to complex agent task execution** (00:00) — Modern language models have evolved from basic factual retrieval to executing complex software engineering workflows like server deployment.
1. **Identifying fragmented context as the primary bottleneck for agents** (03:00) — High tool-calling accuracy reveals that providing the correct data state is the actual rate-limiting factor for autonomous implementations.
1. **Performance limitations of million-token context windows in models** (03:51) — Massive context architectures suffer from the lost in the middle phenomenon where scattered relevant data degrades retrieval performance.
1. **Managing latency and operational costs in transformer attention layers** (05:36) — Processing large contexts exponentially increases matrix operations leading to severe latency and cost bottlenecks.
1. **Designing a real-time context strategy for enterprise workflows** (06:37) — Connecting scattered business data to autonomous agents requires systems configured for rapid retrieval and dynamic navigation boundries.
1. **Optimizing retrieval layers with vector and full-text search** (08:58) — Combining semantic recall with full-text precision limits false positives during dynamic context retrieval.
1. **Building cross-session memory as a product integration moat** (10:07) — Persisting user preferences, changing goals, and historical actions across sessions inherently differentiates competitive artificial intelligence platforms.
1. **Implementing asynchronous memory extraction and lifecycle management pipelines** (11:54) — Isolating short-term context through queued background workers ensures durable facts are safely indexed into long-term databases.
1. **Demonstrating continuous database updates using real-time memory extraction** (14:34) — Real-time background extraction automatically updates schema states without interrupting the current interactive application session.
1. **Creating self-learning agents from historical execution traces** (17:32) — Analyzing prior interactions allows automated systems to discover navigational shortcuts and optimize future execution paths autonomously.
1. **Balancing retrieval precision with context window summarization tradeoffs** (21:28) — Retrieving narrow file scopes instead of full contexts minimizes lossy summarization limits and preserves actionable data fidelity.

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- [Equipping AI agents with memory and context](https://www.wearedevelopers.com/videos/100162-the-private-ai-platform-why-agentic-apps-need-a-private-application-platform) (from "The Private AI Platform: Why Agentic Apps Need a Private Application Platform")
- [Applying context engineering across the full software lifecycle](https://www.wearedevelopers.com/videos/100256-can-this-elephant-dance-ibm-bob-and-the-future-of-ai-first-software-development) (from "Can This Elephant Dance? IBM Bob and the Future of AI-First Software Development")
- [Designing short-term and long-term memory for intelligent agents](https://www.wearedevelopers.com/videos/100318-context-graphs-for-explainable-decision-aware-ai-agents) (from "Context Graphs for Explainable, Decision-Aware AI Agents")
- [Context-aware AI for enterprise business applications](https://www.wearedevelopers.com/videos/100113-building-the-future-of-java-ai-agents-mcp-and-next-gen-app-development) (from "Building the Future of Java: AI Agents, MCP, and Next-Gen App Development")
- [Writing context-dense frontend code for AI agents](https://www.wearedevelopers.com/videos/100180-the-two-trees-of-react) (from "The two trees of React")
- [Modernizing interconnected enterprise systems with context-aware AI](https://www.wearedevelopers.com/videos/1401-the-alpha-developer-of-tomorrow-building-the-future-of-the-software-development-lifecycle) (from "The Alpha‑Developer of Tomorrow: Building the Future of the Software Development Lifecycle")

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