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* refactor(agent): ground runtime decisions in structured evidence Use producer status and structured errors for tool recovery, preserve memory context without lexical confirmation, and remove predetermined receipt and skill conclusions. Require explicit MCP discovery and authenticated schedule trial actions. Preserve authorization, ownership, isolation, and bounded execution; update documentation and regression coverage. * test(sandbox): expect read_file initialization error callback * fix(agent): address evidence review follow-ups * fix(agent): preserve retry budgets for OpenAI transport errors * fix(agent): expose tool execution status in model requests Project validated execution metadata and exact tool text into request-only JSON so status survives provider serialization. Preserve stored results and receipt hashes, cover SDK and graph boundaries, and migrate replay input hashes without changing recorded responses. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
39 lines
2.3 KiB
Markdown
39 lines
2.3 KiB
Markdown
# Run Interaction Policy
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The harness resolves a `RunInteractionPolicy` from the run context and uses it
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as the single source for lead-agent tool visibility, clarification middleware
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behavior, sandbox network approval eligibility, and system-prompt guidance.
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The implementation lives in `../packages/harness/deerflow/agents/interaction_policy.py`.
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Interactive runs inspect available context and proceed with reversible assumptions or independent work when appropriate. Ask when essential information, consequential choices, or authorization for high-risk work is missing; a fixed clarify-plan-act sequence is not required. Clarification interrupts and trust boundaries remain unchanged.
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## Modes and precedence
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Interactive runs may ask a human for clarification. Runs in `scheduled`,
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`webhook`, or `autonomous` mode must use the available context, make minimal
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reversible assumptions, list material assumptions, or return a structured
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`BLOCKED` result naming the missing decision for high-risk or irreversible
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work without sufficient authorization. They must never wait for a synchronous
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human response. Suppressed clarification tool results must preserve this same
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boundary, not tell the model to unconditionally carry out an ambiguous action.
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An explicit `interaction_mode` takes precedence over legacy context hints.
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An unknown explicit mode is a configuration error, never an interactive fallback.
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Without an explicit mode, the resolver checks `non_interactive` (scheduled),
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then `channel_name="github"` (webhook), then `disable_clarification`
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(autonomous); otherwise it selects interactive. These legacy flags remain
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supported while entry points migrate to explicit modes.
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Sync and async sandbox network approval paths use the same resolved policy.
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Unattended runs deny pending network requests without opening a Human Input
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card. Subagents always deny pending requests, even in an interactive run.
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## Gateway trust boundary
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`interaction_mode`, `non_interactive`, `disable_clarification`, and
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`channel_name` are internal-caller-only Gateway context keys. The latter two
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remain runtime-only and are never copied into checkpoint-persisted
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`configurable`. Public run requests cannot use these keys to disable
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clarification, impersonate a webhook channel, or override a scheduled run back
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to interactive behavior.
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