## Context
`AgentAsyncExecutorService.executeAgent` is shared by workflow agent
nodes and the `runAgent` mutation (Slack assistant, apps). Workflow
nodes run a scoped task, so pre-loading the few explicitly-granted
object tools is fast and skips
the `learn_tools` round trip (the behavior settled in #23400 / #23358).
`runAgent` is open-ended: its role grants broad object access, so
pre-loading inlines every CRUD/action schema on every step. That is what
makes the Slack assistant take 3-4 minutes for a prompt Ask AI answers
in seconds. Confirmed still slow with #23400 merged, so this is the
payload, not object scoping.
## What
Add a `toolLoadingStrategy` to `executeAgent` (default `'preload'`, so
workflow nodes and evals are unchanged). `AgentRunService.run` opts into
`'lazy'`, which exposes a compact tool catalog in the system prompt plus
the `learn_tools` / `execute_tool` meta-tools, using composed role
permissions rather than explicit grants only, so the agent keeps broad
access without the full-payload latency.
## How
- Split tool provisioning into two focused methods on the executor; a
3-line dispatch chooses per strategy. The pre-load path is unchanged.
- `buildLazyRegistryToolset`: one reusable definition of lazy registry
provisioning (catalog + meta-tools), so the chat and agent executors can
share it.
- Extract `buildToolCatalogSection` out of `SystemPromptBuilderService`
into a `tool-provider` util so both paths format the catalog identically
(no dup).
- Replace the meta-tools' `excludeTools` denylist with a single
`isToolAllowed` predicate: the agent path passes an allowlist closed
over the shown catalog (enforced at call time), MCP passes its existing
deny predicate.
Workflow node and eval behavior is unchanged.
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Abdul Rahman
ea7863dc4e
