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Lesson 26 — Injected escalation and capability preflight ​

Outcome ​

Let a capable real agent raise Forge Decisions through an injected tool and prove the tool is actually usable before enabling dependent modes.

Why this comes now ​

Watching stdout cannot reveal choices the model considered but never said. Structured escalation must be part of the interaction contract.

Understand ​

Forge registers its own escalation tool for the run and instructs the model to use it. Depending on provider, native question tools may be suppressed/replaced. Registration is not enough: a permission system can deny the tool while the agent silently continues by guessing, so Forge preflights the capability.

Forge configures the individual run, not repository/user-global agent config.

Build the real project ​

  1. Implement the escalation tool server/handler (MCP or provider-native mechanism).
  2. Map tool call → durable Decision before answering/returning.
  3. Support adapter-declared blocking or exit-and-resume semantics.
  4. Add preflight that verifies the tool is actually invocable for the selected Strategy/config.
  5. If preflight fails, disable/refuse escalation-dependent Involvement Modes with a clear reason.
  6. Live-test an ambiguity where the agent asks and obeys an answer contrary to its recommendation.
  7. Test identical task with capability disabled to understand the difference.

Completion gate ​

A real agent raises a Decision; it survives restart; answer is delivered/resumed correctly; a deliberately denied tool fails preflight rather than silently claiming support.

Pitfalls to avoid ​

Do not infer architectural decisions from text. Do not treat “MCP configured” as proof “MCP callable.” Do not modify ~/.claude/repo config to make the integration easy.

References ​

Sculptor substituted tools: https://github.com/imbue-ai/sculptor/blob/main/docs/help/integrated_harnesses.md ; current archived Forge ADR research in docs/archive/01-previous-forge-overview.md.

Checkpoint ​

Record actual escalation behavior/rate in the Learning Log. This is a product hypothesis that must be measured, not assumed.

Forge is local-first. The docs are part of the product engineering system.