Here’s a structured 3-year milestone plan, team structure, and architecture deep dive for your Agentic AI platform:
3-Year Milestones
Theme: From MVP to Autonomous Orchestration
| Year | Phase | Key Milestones | Success Metrics |
|---|---|---|---|
| Year 1 | Core Platform MVP | - Agent framework + Knowledge Graph live - Data ingestion/exception workflows automated - Basic telemetry & audit trails | 50% manual effort reduction in pilot workflows |
| Year 2 | Scale & Self-Service | - Self-service UI for workflow creation - 5+ ops use cases onboarded (e.g., macros, browser agents) - Anomaly detection integrated | 30% workflows built by ops teams |
| Year 3 | Enterprise Autonomy | - Multi-agent collaboration ecosystem - Predictive anomaly resolution - Client onboarding toolkit | 40% MTTR reduction; 10+ cross-functional workflows |
Critical Dependencies:
- Year 1: Secure buy-in from 1-2 ops teams for pilot use cases.
- Year 2: IT integration for legacy systems (e.g., macros, A2A protocols).
- Year 3: Data governance team alignment for knowledge graph scaling.
Team Structure (Platform + Pods Model)
1. Platform Team (Central, Horizontal)
- Agent Framework: Engineers building core agent architecture (LLM integration, rules engine).
- Knowledge Graph: Data engineers/scientists managing entity relationships, anomaly detection.
- Workflow Orchestration: Backend devs for workflow engine, APIs, and registry.
- Observability: DevOps for telemetry, logging, and guardrails (e.g., SLA monitoring).
2. Pods (Vertical, Use-Case Aligned)
- Pilot Pod (Data Ingestion/Exceptions): Ops SMEs + AI engineers automating high-volume workflows.
- Scaling Pod (Browser/Macro Agents): RPA specialists + integration engineers.
- Anomaly Pod: Data scientists + ops analysts refining detection/resolution logic.
Governance Layer (Cross-Cutting)
- Product/Program Mgmt: Roadmap alignment, stakeholder updates.
- AI Governance: Ethics, compliance, and risk oversight.
Key Collaboration Model:
- Platform team provides APIs/tools; pods build use cases.
- Bi-weekly syncs to share reusable agents/patterns.
Architecture Deep Dive
1. Core Layers
- Agent Framework:
- Modular Agents: Rules-based (price checks), LLM-based (root cause analysis), hybrid (anomaly detection).
- Human-in-the-Loop: Approval hooks, validation UI for exceptions.
- Knowledge Graph:
- Entity Resolution: Unifies data from emails, PDFs, etc., into structured relationships.
- Anomaly Detection: Graph embeddings + ML models flag outliers (e.g., unusual trades).
- Workflow Engine:
- Low-Code Designer: Drag-and-drop steps (e.g., "ingest → validate → escalate").
- Registry: Versioned agents/workflows for reuse.
2. Key Components
- Orchestrator: Routes tasks to agents, manages retries/fallbacks.
- Prompt Studio: Templates for natural-language-to-workflow (e.g., "Create exception workflow for top movers").
- Observability Hub:
- Real-time dashboard (e.g., "Agents stuck in loop").
- Drill-down into agent reasoning (e.g., "Why was this trade flagged?").
3. Integration
- Legacy Systems: Browser agents (Playwright/Selenium), macro wrappers.
- Protocols: MCP/A2A adapters for SWIFT, FIX, etc.
- Data Lakes: Pre-processors for unstructured → structured data.
4. Guardrails
- Governance: Role-based access to workflows (e.g., only traders can approve exceptions).
- Explainability: Audit trails show agent decisions + human overrides.
- Rate Limits: Prevent agent cascades (e.g., anomaly detection spamming).
Example Workflow: Exception Resolution
- Trigger: Trade exceeds price tolerance (business rule).
- Agents Activated:
- Data Gatherer: Pulls trade details from emails/PDFs.
- Root Cause Analyzer (LLM): Suggests "liquidity gap in emerging markets."
- Anomaly Detector: Flags related trades in knowledge graph.
- Human Step: Trader validates evidence.
- Close: Audit trail updated; workflow registry logs resolution pattern.
Key Risks & Mitigations
- Risk: Ops teams resist self-service.
Mitigation: Co-develop workflows with pods in Year 1. - Risk: Knowledge graph becomes stale.
Mitigation: Auto-refresh hooks from data sources.
Let me know if you'd like to dive deeper into any area (e.g., agent-KG interaction patterns, specific tech stack choices).