Anthropic is reshaping Agent knowledge management with a three-tier progressive loading mechanism: keeping a minimal catalog permanently resident, loading full rules only when a task matches, and reading scripts and documents only when they are genuinely needed. The signal it sends is clear: as knowledge expands, the bottleneck in AI systems is shifting from model capability to retrieval, routing, and cost control.
What This Is
Dynamic loading—selecting and loading knowledge according to task requirements—does not mean feeding all materials to the model at once. Anthropic’s SKILL.md divides knowledge into three tiers. The first consists of metadata such as names and applicable scenarios, which usually remains permanently resident. The second contains full instructions and is injected once a task matches. The third includes scripts, templates, and reference data, which are read only during actual execution.
Its routing process can be summarized as “search, inspect, select”: the system finds candidate knowledge based on the task, examines the details, and chooses the best-matched content. An Agent—an AI program capable of autonomously planning and calling tools to complete tasks—can therefore “appear to possess many capabilities” while carrying only the genuinely relevant material for each task.
Industry View
We believe the value of this design extends beyond saving tokens—the basic unit used to measure and charge for model text processing. It also reduces enterprise maintenance costs. Adding a business rule no longer requires rewriting an entire system prompt, and when something goes wrong, teams can trace which content was loaded and why. This is especially important for enterprises that frequently adjust workflows.
It is not yet a mature solution that can be copied directly. If metadata is vague, an Agent may select the wrong knowledge; the more detailed the catalog, the more complex the routing decisions become. The size and performance figures cited in the source article come mainly from technical practice and research references, without controlled validation in large-scale production environments. Moreover, models that depend on long-context capabilities may still turn “on demand” into “read everything,” meaning cost savings cannot rely on prompt design alone.
At a deeper level, on-demand loading requires a stable enterprise knowledge foundation. If documents are outdated, versions conflict, or ownership boundaries remain unclear, an Agent will simply retrieve wrong answers faster. Dynamic loading solves the problem of “how to find the material,” but cannot automatically determine whether that material is trustworthy.
Impact on Regular People
For enterprise IT: As enterprises deploy AI assistants, the focus will shift from procuring a single model toward building knowledge catalogs, access controls, audit systems, and update mechanisms. Companies with relatively complete documentation systems may deploy these assistants faster.
For individual professionals: AI assistants will gradually evolve from general-purpose question-answering tools into executors that invoke internal processes according to task. Employees will increasingly need to articulate objectives and confirm boundaries rather than learn complex technical terminology.
For the consumer market: Software models that charge subscriptions by function and load services according to usage will become more common. Consumers may see cheaper, more focused AI features, but whether generated results are reliable will still depend on the maintenance quality of the underlying knowledge base.