74e6bde13a
Five interrelated cleanups:
1. Lead -> Phenomenon provenance
- Phenomenon.from_lead_id field on the dataclass
- BaseAgent.run(lead_id=...) writes self._current_lead_id
- _add_phenomenon auto-injects from agent state (LLM unaware)
- Orchestrator dispatch passes lead.id; Phase 1/2-auto/4/5 stay None
- Merge path preserves the first non-None lead_id on collision
2. Unified Phenomenon <-> Hypothesis link path
- HypothesisAgent only adds hypotheses, never links
- link_phenomenon_to_hypothesis tool + executor removed
- All links go through Orchestrator._judge_new_phenomena
- Phase 2 unconditionally judges after hypothesis generation
- Gap Analysis judges after each dispatch round
(Three previously-missing judge calls now in place.)
3. SSOT in agent subclasses
- Remove RoleTemplate dataclass, ROLE_TEMPLATES dict,
_instantiate_from_template method
- Each agent subclass owns name, role, and tool list
- agent_factory.py shrinks from 299 to 153 lines
- All 7 agents now route through _AGENT_CLASSES (filesystem,
registry, communication, network, timeline were previously dead
subclasses overridden by templates)
4. Configurable edge weights
- HYPOTHESIS_EDGE_WEIGHTS -> _DEFAULT_EDGE_WEIGHTS (private default)
- EvidenceGraph(edge_weights=...) override via config.yaml
- hypothesis_edge_weights section in config.yaml (commented example)
- main.py and regenerate_report.py read and pass through
5. regenerate_report.py auto-picks the latest run/*/graph_state.json
when no CLI arg is given (was a hardcoded date path)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
"""Hypothesis Agent — generates investigative hypotheses from phenomena.
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Generates hypotheses only. Phenomenon→Hypothesis linking is handled centrally
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by Orchestrator._judge_new_phenomena, so all link logic lives in one place.
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"""
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from __future__ import annotations
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import logging
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from base_agent import BaseAgent
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from evidence_graph import EvidenceGraph
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from llm_client import LLMClient
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logger = logging.getLogger(__name__)
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class HypothesisAgent(BaseAgent):
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name = "hypothesis"
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role = (
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"Hypothesis analyst. You review all phenomena discovered so far "
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"and formulate investigative hypotheses about what happened on this system. "
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"Your ultimate goal: build the most complete picture of events that occurred."
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)
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def __init__(self, llm: LLMClient, graph: EvidenceGraph) -> None:
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super().__init__(llm, graph)
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self._register_hypothesis_tools()
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def _register_hypothesis_tools(self) -> None:
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self.register_tool(
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name="add_hypothesis",
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description=(
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"Create a new investigative hypothesis about what happened on the system. "
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"Each hypothesis should be a specific, testable claim."
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),
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input_schema={
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"type": "object",
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"properties": {
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"title": {
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"type": "string",
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"description": "Short title for the hypothesis.",
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},
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"description": {
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"type": "string",
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"description": "Detailed description of what this hypothesis claims.",
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},
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},
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"required": ["title", "description"],
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},
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executor=self._add_hypothesis,
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)
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async def _add_hypothesis(self, title: str, description: str) -> str:
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hid = await self.graph.add_hypothesis(
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title=title,
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description=description,
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created_by=self.name,
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)
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return f"Hypothesis created: {hid} — {title} (confidence: 0.50)"
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