Context engineering is emerging as the core systems layer in production AI agents.

In multi-step agents with tool calls, token growth is exponential. Without deliberate context control, agents degrade over time through context rot, window overflow, or compaction loss.

The companies building the most capable AI agents today: Manus, Cursor, Anthropic AI, OpenAI, Google DeepMind, LangChain are all solving the same problem: what information should an LLM see, when should it see it, and how should it be structured?

This session covers the invisible layer that makes or breaks production:
- Context window lifecycle management
- In-loop retrieval vs static RAG
- Logit masking and MCP tool gating strategies
- Memory compaction and state consolidation
- Multi-agent context isolation
- Cost-performance trade-offs in long-running trajectories

We will also examine a comparative analysis of how leading AI teams from Manus, OpenAI, Cursor etc. approach these design decisions where their strategies converge, where they conflict, and what practical lessons emerge.

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Supporting Links:
1. How Top AI Companies Handle Context Engineering- https://x.com/Hxlfed14/status/2022984467380682856
2. Advanced Context Engineering Techniques: A Technical Deep Dive- https://medium.com/@himanshusangshetty/advanced-context-engineering-techniques-a-technical-deep-dive-b997e74cab92