The Multi-Agent Architecture
Autonomous GTM requires a coordinated team of specialized agents — each owning a specific revenue function — working under shared constraints: Tokenization, Proof of Intent (PoI), and GTM Math.
Why GTM Requires Specialization (Not a Monolith)
GTM is a sequence of interdependent workflows: Enrichment → Signal Interpretation → Prioritization → Planning → Execution → Learning Loop. A single, general-purpose model cannot perform all of this reliably or transparently.
Governability
Narrow agents are easier to constrain, audit, and improve. PoI ties their actions directly to explainable reasoning.
Transparency
Each agent’s decision path is local and explainable—no black-box drift, no hallucinated reasoning.
Performance
Purpose-built agents optimize deeply for their GTM role instead of trying to do everything generically.
Specialization is what transforms "automation" into safe, enterprise-grade autonomy.
The Core GTM Agent Set (Version 1)
These agents are Fairway’s foundational agents—all operating using the shared GTM Math, Governance Stack, and Tokenized Alignment.
1. Outbound Execution Agent
Mission: Build, schedule, and optimize outbound sequences. Logs PoI for every message and earns tokens based on quality, timing, and compliance.
2. ABM Program Agent
Mission: Orchestrate multi-channel, account-based plays. Owns persona strategy, narratives, and timing logic using GTM Math.
3. Research & Enrichment Agent
Mission: Keep data accurate, enriched, and contextualized. Validates contacts, enriches 'Talking Points,' and prevents other agents from using stale data.
4. Signal Correlation Agent
Mission: Interpret user behavior and identify real buying intent. Applies Fit × Intent × Timing scoring models and flags risk.
5. Forecasting & Health Agent
Mission: Score opportunities, estimate deal velocity, and predict risk. Uses GTM Math to track pipeline deterioration early.
The Coordination Pattern: How Agents Work Together
The system runs on a predictable rhythm—a self-correcting loop—to maximize transparency and productivity.
1. Signals & Data: Signal Correlation Agent + Research Agent detect, enrich, and validate data.
2. Prioritization: GTM Math ranks accounts using Fit × Intent × Timing × Value.
3. Planning: ABM Program Agent generates narratives, sequences, and account plays.
4. Execution: Outbound Agent sends sequences and logs PoI justification for every action.
5. Learning Loop: Forecasting Agent logs outcomes, updates opportunity scores, and pushes data back into the substrate.
This loop is what transforms autonomy from "automation" into a reliable revenue operating machine.
The Supervisor Pattern (Guardrail Layer)
A thin orchestration layer—not a monolithic "parent agent." It prevents chaos by enforcing global business rules, resolving conflicts, and escalating risk.
Enforces Rules
System-wide constraints: email limits, regional laws, persona restrictions, and compliance boundaries.
Resolves Conflicts
When multiple agents attempt to act on the same account, the Supervisor uses GTM Math to break ties.
Controls Escalation
Low-confidence or high-risk situations are routed to humans with PoI reasoning attached.
Example: A Multi-Agent GTM Workflow in Action
Scenario: Target account shows rising intent around “data governance for AI.”
- **Signal Correlation Agent:** Detects multi-signal intent; assigns Tier 1 status (PoI explains why).
- **Research Agent:** Validates personas, adds new contextual info (“announced new AI initiative last quarter”).
- **ABM Program Agent:** Designs account play: messaging frameworks, risk themes, narrative timing.
- **Outbound Execution Agent:** Sends sequences with PoI justification: why this message, why now, via which channel.
- **Forecasting Agent:** Tracks progression, updates opportunity score dynamically as replies, meetings, and signals evolve.
Throughout: Tokenization rewards accurate, compliant behavior. PoI ensures transparent, auditable reasoning. GTM Math governs prioritization and thresholds.
Why This Model Is Safer and More Productive
Compared to a generic “GTM bot,” the multi-agent architecture provides:
Higher Performance
Agents specialize deeply, leading to optimized execution per workflow.
Higher Control
Behavior is constrained, auditable, and easier to improve.
Higher Trust
PoI + Tokenization enforce explainability and alignment.
Easier Scaling
New agents can be added as modular components, without system refactoring.
The Multi-Agent Architecture is the execution engine of the Operating Model.
Back to Operating Model OverviewProceed to: The Governance Stack