If we want them to do Knowledge Work, design them as Knowledge Agents — Benjamin Clavié, Mixedbread
The talk distinguishes between 'Coding Agents' and 'Knowledge Agents,' arguing that most real-world tasks fall under the latter. While code provides durable cues (identifiers, file paths), knowledge work is inherently ambiguous, diffuse, and context-dependent (e.g., legal or medical research). The speaker posits that AI agents must be designed to mimic human knowledge work patterns—specifically, through advanced orchestration. This involves breaking down complex, open-ended problems, utilizing multiple specialized tools (primitives like BM25 and semantic search), and employing sub-agents (searchers) to synthesize findings into memos, thereby reducing the 'oracle gap' between perfect knowledge and the agent's output.
Key takeaways
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Knowledge Work vs. Coding Work
Coding is a special, easy case because code has durable cues (identifiers, method definitions). Knowledge work, however, is defined by ambiguous information input and requires reconstructing intent and judgment, making it significantly harder for agents.
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The Knowledge Loop
9:15
Human progress in knowledge work is driven by a single self-optimizing loop: better tools create new roles, new roles generate knowledge, and new knowledge demands better tools. This pattern should guide agent design.
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The Necessity of Orchestration
Effective agent performance requires more than just a single tool. The most significant gains come from architectural improvements, such as having a main agent delegate tasks to specialized 'searcher agents' that return structured memos, which reduces the 'oracle gap' (the difference between perfect information and the system's output) by up to 40%.
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Tooling is not Neutral
Tools are not merely incremental improvements; they are critical for overcoming performance ceilings. The ability to use a tool (e.g., a library catalog vs. physically searching archives) determines if a task is scalable and cheap enough to be practical.