Strategy Module -- Trendslop Mitigation¶

Structural mitigation against LLM tendency to recommend trendy, context-insensitive strategies ("trendslop") for strategic agent roles.

Module: src/synthorg/engine/strategy/ Phase 1: Core models, config, prompt integration (this page) Phase 2: Meeting integration (#1158)

Background¶

HBR research (March 2026) shows LLMs systematically recommend trendy, context-insensitive strategies across 7 core business tensions. Prompt-level fixes produce < 2% bias reduction. SynthOrg mitigates this structurally through constitutional principles, multi-lens analysis, confidence calibration, and output mode control.

Strategic Output Modes¶

Controls how strategic agents frame their recommendations. Set per-agent via AgentIdentity.strategic_output_mode or company-wide via strategy.output_mode.

Resolution: agent override > config default. context_dependent resolves to decision_maker for C-suite/VP, advisor otherwise.

Strategic Lenses¶

8 analysis perspectives forced on strategic agents:

Default (always active)¶

Optional (enabled via config)¶

Constitutional Principles¶

Anti-trendslop rules loaded from YAML packs and injected into system prompts. Each principle has an ID, text, category, and severity level (informational, warning, critical).

Built-in Packs¶

Pack Schema¶

name: "pack-name"
version: "1.0.0"
description: "Pack description"
principles:
  - id: "principle_id"
    text: "Rule text injected into prompts"
    category: "category_name"
    severity: "critical"  # informational | warning | critical

User packs: ~/.synthorg/strategy-packs/<name>.yaml (override builtins by name).

Confidence Calibration¶

Strategic agents must provide calibrated confidence with every recommendation:

Impact Scoring¶

7-dimension weighted scoring determines the appropriate level of strategic analysis:

Weights must sum to 1.0. Composite score maps to cost tiers via thresholds.

Cost Tier Resolution¶

Resolution: ProgressiveTierResolver (score-based) or FixedTierResolver (config-based).

Prompt Injection¶

Strategic sections are injected into the system prompt after autonomy instructions, before the task section. Injection occurs when:

1. Agent has explicit strategic_output_mode, OR
2. Agent seniority is C-suite, VP, or Director

Injected Sections¶

1. Strategic Analysis Framework -- maturity stage, industry, competitive position
2. Constitutional Principles -- anti-trendslop rules from active pack
3. Contrarian Analysis -- forced opposite-case consideration
4. Confidence Calibration -- output format requirements
5. Assumption Surfacing -- explicit assumption listing
6. Output Requirements -- mode-specific output instructions

The strategy section is trimmable (removed first when over token budget).

Config Shape¶

strategy:
  output_mode: "advisor"
  cost_tier: "moderate"
  default_lenses:
    - contrarian
    - risk_focused
    - cost_focused
    - status_quo
  constitutional_principles:
    pack: "default"
    custom: []
  confidence:
    format: "structured"
  consensus_velocity:
    action: "devil_advocate"
    threshold: 0.85
  premortem:
    participants: "all"
  conflict_detection:
    strategy: "auto"
  context:
    source: "config"
    maturity_stage: "growth"
    industry: "technology"
    competitive_position: "challenger"
  progressive:
    weights:
      budget_impact: 0.2
      authority_level: 0.15
      decision_type: 0.15
      reversibility: 0.2
      blast_radius: 0.1
      time_horizon: 0.1
      strategic_alignment: 0.1
    thresholds:
      moderate: 0.4
      generous: 0.7

Decision Records¶

DecisionRecord includes three optional strategy metadata fields. All are nullable and default to None (or () for tuples) -- they are only populated when the strategy module is active during the meeting that produces the decision.

RiskCard¶

Per-decision risk metadata (populated by premortem phase): - decision_type: Type of decision - reversibility: easily_reversible / moderate / locked_in - blast_radius: individual / team / department / company_wide - time_horizon: immediate / short_term / medium_term / long_term

ConfidenceMetadata¶

Calibrated confidence for a recommendation: - level: Point estimate (0.0-1.0) - range_lower, range_upper: Confidence range - assumptions: Key assumptions - uncertainty_factors: Uncertainty sources

LensAttribution¶

Which lens produced which insight (tuple, one per participant): - lens: Lens name - insight: Lens-specific insight - weight: Influence on final recommendation

Architecture¶

Protocol Pattern¶

All major components are pluggable behind @runtime_checkable Protocol:

Module Layout¶

engine/strategy/
  __init__.py         -- Public exports
  models.py           -- Config + domain models (frozen Pydantic)
  lenses.py           -- StrategicLens enum + definitions
  principles.py       -- Pack loading service
  context.py          -- Context providers
  impact.py           -- Impact scorers
  confidence.py       -- Confidence formatters
  output.py           -- Output mode handler
  tiers.py            -- Cost tier resolvers
  prompt_injection.py -- Prompt section builder
  packs/              -- Built-in YAML principle packs
    default.yaml
    startup.yaml
    enterprise.yaml
    cost_sensitive.yaml

References¶

• Research: #693
• Phase 2 (meeting integration): #1158
• HBR trendslop article: March 2026