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287
thirdeye/backend/agents/cross_group_analyst.py
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287
thirdeye/backend/agents/cross_group_analyst.py
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"""Cross-Group Analyst Agent — detects blind spots between multiple teams."""
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import logging
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from backend.providers import call_llm
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from backend.db.chroma import get_all_signals, get_group_ids
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from backend.db.models import CrossGroupInsight
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from backend.agents.json_utils import extract_json_object
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logger = logging.getLogger("thirdeye.agents.cross_group_analyst")
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SYSTEM_PROMPT = """You are the Cross-Group Intelligence Analyst for ThirdEye. This is the MOST IMPORTANT analysis.
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You receive intelligence summaries from MULTIPLE Telegram groups. Your job is to find BLIND SPOTS — information in one group that should be in another.
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Detect:
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- blocked_handoff: Team A waiting for something from Team B, but Team B doesn't know
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- conflicting_decision: Team A decided X, Team B decided the opposite
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- information_silo: Critical info in Group A never reached Group B
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- promise_reality_gap: Promise made in one group, but another group shows it's blocked
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- duplicated_effort: Two teams working on similar things unknowingly
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Respond ONLY with valid JSON (no markdown):
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{"insights": [{"type": "insight_type", "description": "SPECIFIC description naming the groups, people, and topics", "group_a": {"name": "group_name", "evidence": "what was said"}, "group_b": {"name": "group_name", "evidence": "what was said or NOT said"}, "severity": "warning|critical", "recommendation": "Specific action"}]}
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If no cross-group issues: {"insights": []}
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Be SPECIFIC. Name the groups, people, topics, and exact conflicts."""
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def _heuristic_cross_group_insights(
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group_summaries: dict[str, list[dict]],
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) -> list[CrossGroupInsight]:
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"""Generate best-effort cross-group insights when LLM output is unavailable."""
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insights: list[CrossGroupInsight] = []
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normalized = {}
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for group_name, signals in group_summaries.items():
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docs = [str(s.get("document", "")) for s in signals]
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combined = " ".join(docs).lower()
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signal_types = []
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for s in signals:
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signal_types.append(
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str(s.get("metadata", {}).get("type", "unknown")).lower()
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)
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normalized[group_name] = {
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"text": combined,
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"signals": signals,
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"types": signal_types,
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}
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group_names = list(normalized.keys())
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for i in range(len(group_names)):
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for j in range(i + 1, len(group_names)):
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group_a = group_names[i]
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group_b = group_names[j]
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text_a = normalized[group_a]["text"]
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text_b = normalized[group_b]["text"]
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types_a = set(normalized[group_a]["types"])
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types_b = set(normalized[group_b]["types"])
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# Detect a likely blocked handoff around design/spec dependencies.
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a_waiting = any(
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k in text_a for k in ["waiting", "blocked", "design spec", "specs"]
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)
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b_mentions_specs = any(
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k in text_b for k in ["design spec", "specs", "design"]
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)
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if a_waiting and not b_mentions_specs:
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insights.append(
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CrossGroupInsight(
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type="blocked_handoff",
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description=(
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f"{group_a} indicates dependency blockage (design/spec inputs), "
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f"but {group_b} has no corresponding discussion of that dependency."
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),
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group_a={
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"name": group_a,
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"evidence": "Contains waiting/blocked language tied to specs or design dependency.",
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},
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group_b={
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"name": group_b,
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"evidence": "No clear mention of design specs/dependency handoff in available signals.",
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},
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severity="warning",
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recommendation=(
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f"Create a shared handoff item between {group_a} and {group_b} for design/spec ownership "
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"with an explicit due date."
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),
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)
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)
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# Detect likely promise vs execution mismatch.
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b_promises = any(
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k in text_b
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for k in ["demo", "friday", "promised", "told the client", "ready by"]
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)
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a_blocked = any(
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k in text_a
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for k in ["blocked", "waiting", "can't proceed", "cannot proceed"]
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)
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if b_promises and a_blocked:
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insights.append(
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CrossGroupInsight(
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type="promise_reality_gap",
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description=(
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f"{group_b} signals delivery promises while {group_a} reports blockers that may prevent those commitments."
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),
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group_a={
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"name": group_a,
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"evidence": "Signals include active blockers/waiting dependencies.",
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},
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group_b={
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"name": group_b,
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"evidence": "Signals include explicit client/demo commitments and timelines.",
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},
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severity="critical",
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recommendation="Run a joint risk review and re-baseline commitments before the next client update.",
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)
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)
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# Type-based silo detection when lexical cues are weak.
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a_operational_risk = bool(
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types_a.intersection(
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{"recurring_bug", "workaround", "tech_debt", "deployment_risk"}
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)
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)
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b_planning_focus = bool(
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types_b.intersection(
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{
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"feature_request",
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"roadmap_drift",
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"priority_conflict",
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"user_pain_point",
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}
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)
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)
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if a_operational_risk and b_planning_focus:
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insights.append(
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CrossGroupInsight(
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type="information_silo",
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description=(
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f"{group_a} shows operational risk signals while {group_b} is focused on planning/user demands, "
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"suggesting risk context is not shared across groups."
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),
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group_a={
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"name": group_a,
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"evidence": f"Operational risk signal types: {sorted(types_a.intersection({'recurring_bug', 'workaround', 'tech_debt', 'deployment_risk'}))}",
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},
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group_b={
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"name": group_b,
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"evidence": f"Planning-focused signal types: {sorted(types_b.intersection({'feature_request', 'roadmap_drift', 'priority_conflict', 'user_pain_point'}))}",
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},
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severity="warning",
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recommendation="Add a weekly cross-functional risk sync so product planning reflects current engineering constraints.",
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)
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)
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# Check reverse direction as well.
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b_operational_risk = bool(
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types_b.intersection(
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{"recurring_bug", "workaround", "tech_debt", "deployment_risk"}
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)
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)
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a_planning_focus = bool(
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types_a.intersection(
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{
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"feature_request",
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"roadmap_drift",
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"priority_conflict",
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"user_pain_point",
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}
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)
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)
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if b_operational_risk and a_planning_focus:
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insights.append(
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CrossGroupInsight(
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type="information_silo",
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description=(
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f"{group_b} shows operational risk signals while {group_a} is focused on planning/user demands, "
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"suggesting risk context is not shared across groups."
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),
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group_a={
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"name": group_b,
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"evidence": f"Operational risk signal types: {sorted(types_b.intersection({'recurring_bug', 'workaround', 'tech_debt', 'deployment_risk'}))}",
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},
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group_b={
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"name": group_a,
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"evidence": f"Planning-focused signal types: {sorted(types_a.intersection({'feature_request', 'roadmap_drift', 'priority_conflict', 'user_pain_point'}))}",
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},
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severity="warning",
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recommendation="Add a weekly cross-functional risk sync so product planning reflects current engineering constraints.",
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)
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)
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deduped = []
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seen_keys = set()
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for insight in insights:
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key = (insight.type, insight.group_a.get("name"), insight.group_b.get("name"))
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if key in seen_keys:
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continue
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seen_keys.add(key)
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deduped.append(insight)
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return deduped[:5]
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async def analyze_cross_group(
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group_summaries: dict[str, list[dict]] = None,
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) -> list[CrossGroupInsight]:
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"""
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Analyze intelligence across all monitored groups to find blind spots.
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Args:
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group_summaries: Optional pre-built summaries. If None, loads from ChromaDB.
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"""
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if group_summaries is None:
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group_ids = get_group_ids()
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if len(group_ids) < 2:
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logger.info("Need at least 2 groups for cross-group analysis")
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return []
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group_summaries = {}
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for gid in group_ids:
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signals = get_all_signals(gid)
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group_summaries[gid] = signals
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if len(group_summaries) < 2:
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return []
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# Format summaries for the LLM
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summary_parts = []
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for group_name, signals in group_summaries.items():
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signal_lines = []
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for s in signals[:30]: # Limit per group to fit context
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meta = s["metadata"]
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signal_lines.append(f" - [{meta.get('type', '?')}] {s['document'][:120]}")
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summary_parts.append(
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f"=== GROUP: {group_name} ({len(signals)} total signals) ===\n"
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+ "\n".join(signal_lines)
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)
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full_summary = "\n\n".join(summary_parts)
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{
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"role": "user",
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"content": f"Analyze cross-group intelligence:\n\n{full_summary}",
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},
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]
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try:
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result = await call_llm(
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"reasoning",
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messages,
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temperature=0.2,
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max_tokens=2000,
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response_format={"type": "json_object"},
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)
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parsed = extract_json_object(result.get("content", ""))
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insights = []
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for i in parsed.get("insights", []):
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insights.append(
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CrossGroupInsight(
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type=i.get("type", "unknown"),
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description=i.get("description", ""),
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group_a=i.get("group_a", {}),
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group_b=i.get("group_b", {}),
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severity=i.get("severity", "warning"),
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recommendation=i.get("recommendation", ""),
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)
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)
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logger.info(f"Cross-group analysis found {len(insights)} insights")
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return insights
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except Exception as e:
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raw = ""
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if "result" in locals() and isinstance(result, dict):
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raw = str(result.get("content", ""))[:300].replace("\n", " ")
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logger.info(f"Cross-group LLM parse issue, using fallback: {e}; raw_head={raw}")
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fallback = _heuristic_cross_group_insights(group_summaries)
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if fallback:
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logger.info(
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f"Cross-group heuristic fallback produced {len(fallback)} insights"
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)
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return fallback
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