Why This Session Matters
Enterprise AI teams are moving into agentic workflows that connect to tools, data, and internal systems. In regulated industries, the main challenge is not only what agents can do, but how safely they can do it.
In this session, you’ll learn how to:
- Understand where MCP fits in enterprise AI architectures
- Design agentic workflows with compliance and governance from day one
- Move beyond basic RAG toward tool-using, context-aware AI systems
- Identify business and technical risks before scaling AI agents
- Apply lessons learned from MCP and agentic AI projects in regulated industries
- Build internal confidence for production AI approval, not just experimentation
Who Should Watch
Built for technical and business leaders responsible for moving AI into production:
- CTOs and technology executives
- Heads of AI, ML, and data science
- AI engineering and platform leaders
- Enterprise architects
- Life sciences, pharma, healthcare, and finance teams
- Teams scaling from RAG pilots to governed agentic AI systems
This session is especially relevant if your organization is asking:
- “How do we move beyond RAG without increasing compliance risk?”
- “How do we safely let AI agents access enterprise systems?”
- “Where does MCP actually fit in our architecture?”
- “What governance layer do we need before deploying agentic workflows?”
- “How do we prove reliability, auditability, and business value?”
Meet the Speakers
Senior & Technical Leaders, Anthropic
To be announced
Representatives from Anthropic will join the session to share the partner perspective on MCP, enterprise AI adoption, and the architectural considerations behind safe, scalable AI systems.
Mateusz Kwaśniak
Senior Tech Lead, deepsense.ai
Mateusz will lead the session and share practical architecture lessons from building production-grade AI systems, including MCP-based integrations and governed agentic workflows for regulated and enterprise environments.
He will focus on the technical blueprint behind safe AI deployment: how MCP fits into the broader system, what controls are needed around it, and how teams can design AI agents that are reliable, auditable, and ready for production use.
Max Pilzys
Machine Learning Engineer, deepsense.ai
Max will join the session to bring an implementation and delivery perspective on how organizations should evaluate MCP, agentic workflows, and compliance-first AI architecture in real business environments.
He will help connect the technical architecture to executive concerns: risk, approval paths, operational readiness, and measurable business value.
Why join deepsense.ai session
deepsense.ai helps organizations design, build, and scale AI systems that deliver measurable ROI – reliably, securely, and in production. Our work spans AI strategy, solution development, AI engineering teams, and AI operations, with a strong focus on production-grade systems for agentic AI, RAG, voice AI, and evaluation.
We bring experience from 200+ commercial AI projects, 120 AI experts, and strategic partnerships with leading AI ecosystem players, including OpenAI, Anthropic, Google Cloud, AWS, and ElevenLabs.
Our recent work includes production AI agents, enterprise RAG systems, voicebots, evaluation frameworks, and AI solutions for pharma, healthcare, financial services, manufacturing, SaaS, and other data-intensive industries.
