Why Multi-Agent Research?
Traditional equity research suffers from three structural weaknesses: confirmation bias, information silos, and inconsistent methodology. Mycroft's architecture addresses each by decomposing the research process into specialized, adversarial agents that must reach consensus before publishing a view.
The Agent Roster
Sentinel — Market Surveillance
Sentinel continuously monitors 15,000+ data feeds including SEC filings, earnings transcripts, patent databases, satellite imagery, and social sentiment. It generates "signal packets" — structured alerts that trigger deeper investigation by downstream agents.
Analyst — Fundamental Modeling
The Analyst agent builds and maintains dynamic financial models for covered companies. Unlike static spreadsheet models, these update in real-time as new data arrives, automatically flagging when assumptions deviate from actuals by more than 2 standard deviations.
Auditor — Forensic Accounting
Our most skeptical agent. The Auditor applies Beneish M-Score analysis, Altman Z-Score monitoring, and custom forensic screens to every company in coverage. It has flagged 14 material accounting irregularities in the past 12 months before they became public.
"The Auditor's job is to find reasons NOT to invest. If it can't, that's a strong signal." — Mycroft Architecture Team
Strategist — Macro & Thematic
The Strategist agent maintains a real-time macro framework incorporating 200+ economic indicators, central bank communications, and geopolitical risk factors. It provides the top-down context that grounds bottom-up stock picks.
The Consensus Mechanism
No single agent can publish a research view. Instead, Mycroft uses an adversarial consensus protocol:
1. Signal Detection: Sentinel identifies a potential opportunity or risk
2. Deep Analysis: Analyst builds the fundamental case
3. Stress Testing: Auditor attempts to falsify the thesis
4. Contextualization: Strategist validates macro alignment
5. Scoring: All agents contribute to a weighted conviction score (1-10)
Only ideas scoring above 7.0 are published. The average published idea scores 8.2, reflecting the high bar set by adversarial review.
Transparency & Reproducibility
Every Mycroft research output includes a full audit trail: which agents contributed, what data sources were used, where agents disagreed, and how the final conviction score was calculated. This level of transparency is unprecedented in both human and AI-driven research.
What This Means for Subscribers
Mycroft subscribers don't just get stock picks — they get a repeatable, transparent research process that improves with every iteration. Our agents learn from prediction errors, continuously refining their models and expanding their data coverage.
This methodology overview reflects Mycroft's current production architecture as of Q1 2026.