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An AI agent that can send an email still needs to know whether to send it, who should receive it, and what to do if the information is incomplete. Tool access lets an agent act; know-how is the procedure and judgment that guide those actions.
What tool access gives an AI agent—and what it does not
Tools connect an agent to available actions: searching contacts, sending messages, updating a customer relationship management (CRM) system, or checking campaign statistics. Credentials and permissions determine which actions it can take. Neither access nor a long list of integrations explains which action is appropriate in a particular situation.
Know-how supplies that missing direction. It tells the agent what it is trying to achieve, what context matters, how to choose and sequence actions, which constraints to respect, and how to judge the result. Jun Wang captures the distinction in the line “MCP connects. Skills describe.” in his article on tools and know-how. The phrase is Wang’s summary, not a statement from a standards body.
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“Why 100 tools is not 100x capability” is a useful way to frame the problem, not a measured ratio. Adding tools expands the actions an agent might take, but it also gives the agent more choices to evaluate. Without a clear procedure, it may choose an unsuitable action, do unnecessary or duplicate work, or fail to establish whether the task succeeded. These are risks in Wang’s analysis, not results of a reported experiment.
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Consider Wang’s illustrative sales-agent scenario. An agent can find contacts, send messages, build outreach sequences, update a CRM, and inspect campaign statistics. Those abilities alone do not tell it which prospects fit the market, what message is appropriate, when follow-up makes sense, or what the campaign results should change. The example explains the argument; it is not a documented test of an agent.
What a useful procedure needs to specify
A prompt can give a one-time instruction. A reusable procedure—or skill—needs enough structure to guide decisions across the steps and handle cases that do not go as planned. Wang’s article describes a useful skill in terms of the following elements:
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- Objective: The intended outcome, stated clearly enough to distinguish completion from activity. “Send a message” is an action; “contact eligible prospects with an approved introduction and record the result” is closer to an outcome.
- Required context: The information the agent needs before acting, such as the prospect’s role, relevant account history, or the conditions that make someone eligible.
- Decision rules: How to determine whether a case qualifies, what to do when information conflicts, and when to stop rather than guess.
- Tool choice and order: Which available tool serves each step, and what must happen before another action. For example, verify eligibility before creating a sequence or sending a message.
- Constraints: Business rules that shape the work, including approved messaging, limits on outreach, and records that must be updated.
- Exception handling: What to do when a contact is missing, a tool fails, data disagrees, or an action requires a person’s decision.
- Expected output: The record, draft, report, or other deliverable the process should produce, including what information it should contain.
- Evaluation: How to check whether the intended outcome was reached and whether the work followed the constraints—not merely whether the agent called a tool.
These components turn a sequence of possible actions into a repeatable way of working. They also expose gaps: if no rule says what to do with conflicting data, the agent has no reliable basis for resolving it.
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Instructions do not grant permissions
A procedure can guide an agent to use a capability it already has. It does not grant access to a mailbox, CRM, or other system. That boundary belongs in the tools and credentials: the agent’s available integrations and the permissions attached to them determine what it is authorized to do.
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Keep the two layers distinct. Use instructions to define appropriate behavior; use access controls to limit available authority. If an agent should prepare messages but not send them, a procedure can specify a draft-and-review workflow, while the system’s permissions should prevent unauthorized sending where the software supports that restriction. A written instruction alone is not an access control.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to tell whether a procedure is ready
Wang proposes a practical heuristic: could a competent new hire follow the procedure and reach a similar result on a bad day? This is a useful test, not a validated assessment standard. Apply it by checking whether the procedure makes the work understandable even when context is incomplete or an ordinary step fails:
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- Can someone identify the goal and the information needed to begin?
- Are choices and stopping conditions explicit, rather than left to guesswork?
- Does the procedure say which tools to use and in what order?
- Can the agent distinguish actions it is instructed to take from actions it is authorized to take?
- Does it cover common exceptions and specify the expected result?
- Is there a way to evaluate the outcome and detect incomplete or incorrect work?
If the answers are unclear, adding more tools is unlikely to fix the underlying gap. Improve the procedure first, then check whether the agent has only the access it needs and whether the outcome can be evaluated.
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Why practical skill matters
Knowing what an object can do is different from knowing how to use it toward a goal. A scholarly chapter on affordances makes this distinction with an example attributed to Edward S. Reed: “It is one thing to know that a needle is sharp and affords poking a hole; it is quite another thing altogether to know how to use it to sew anything” (Reed 1996, p. 123, as quoted in “Tool and Machine: The Affordances of the Mobile Phone”). The same idea applies to agents: access to a tool describes a possibility, while know-how describes purposeful use.
Wang’s article does not provide a named statistic or dated numerical finding showing how much a procedure improves agent performance. Its case is conceptual and practical: reliable work depends not only on what an agent can access, but also on how it decides, acts, handles exceptions, and checks the result.
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