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@dreski
Large language models, with their expanding context windows, tool integrations, and memory strategies, increasingly resemble general-purpose reasoning systems capable of automating a wide range of tasks. In constrained, short-term interactions, they can simulate intelligent assistance with notable effectiveness. However, this performance does not extend seamlessly to longer-term conversations that span diverse domains, formats, or evolving models. In such cases, the absence of durable context management structures becomes apparent.
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What is lacking is not model capacity, but the supporting strategies and engineering practices necessary to sustain coherent agent behavior over time. The ability to capture, abstract, retrieve, and reintroduce relevant context into model inputs is essential for continuity and alignment. Just as the development of the steam engine did not directly result in the creation of the motor car, the availability of powerful LLMs alone does not produce fully realized agentic systems. These systems require deliberate architectural design to manage information lifecycles, route tasks effectively, and maintain stable agent identities.
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At the same time, LLMs are becoming increasingly fungible. Their interchangeability is driven by a growing ecosystem of models optimized for specific domains, languages, or operational constraints. Fine-tuned models trained on proprietary data will be employed to reduce hallucinations and improve relevance, while others will be selected for their proficiency in particular human languages, programming paradigms, or collaborative workflows. In such an environment, agents will rely not on a single general-purpose model but on coordinated systems of specialized models.
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