Agentic AI / MCP

The future of business automation isn't just AI—it's autonomous AI agents that can reason, plan, and act independently. I help B2B SaaS companies and Financial Services firms harness agentic AI to drive competitive advantage and operational efficiency.

What is Agentic AI?

Unlike traditional AI that simply predicts or classifies, agentic AI systems:

  • Reason through complex problems autonomously
  • Plan multi-step workflows and adapt to changing conditions
  • Act by executing tasks and making decisions
  • Learn from outcomes to improve performance

This represents a fundamental shift from AI-as-a-tool to AI-as-a-workforce.

Services

AI Strategy & Roadmap

For Scale-ups & Growth Companies:

  • AI readiness assessment and maturity modeling
  • Use case identification and prioritization
  • ROI modeling and business case development
  • Technology stack selection and vendor evaluation
  • Risk assessment and governance frameworks

For Financial Services:

  • Regulatory compliance and AI governance (MiFID II, GDPR, etc.)
  • Risk management and model validation
  • Client-facing AI applications (advisory, research, trading)
  • Back-office automation (KYC, compliance, operations)

AI Enablement & Training

Executive Workshops

  • • Agentic AI fundamentals for C-suite
  • • Strategic implications
  • • Investment decisions

Technical Team Training

  • • AI agent development
  • • Multi-agent system design
  • • MCP implementation

Bespoke AI Agent Development

I design and build production-ready AI agent systems tailored to your business:

Single Agent Systems

  • • Customer support automation
  • • Document analysis
  • • Research gathering
  • • Financial analysis

Multi-Agent Systems

  • • Coordinated workflows
  • • Agent orchestration
  • • Complex decision-making
  • • End-to-end automation

MCP Server Development

  • • Enterprise integration
  • • Secure data access
  • • Tool creation
  • • Cross-platform capabilities

Technology Stack

  • LLM Providers: OpenAI, Anthropic (Claude), Google, Azure OpenAI
  • Agent Frameworks: LangChain, LangGraph, AutoGen, CrewAI
  • MCP: Model Context Protocol implementation
  • Infrastructure: AWS, Azure, GCP cloud platforms

Engagement Models

Discovery & Strategy (2-4 weeks)

Current state assessment, use case identification, roadmap development

Fixed fee

Proof of Concept (4-8 weeks)

Single agent prototype, validation with real data

Fixed fee or hourly

Full Implementation (3-6 months)

Production-ready systems, integration, training

Project-based or retainer

Fractional AI Leadership

Part-time AI strategy and execution (1-3 days/week)

Monthly retainer

Ready to explore agentic AI for your business?

Let's discuss how autonomous AI agents can transform your operations and create competitive advantage.

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