Agentic AI architecture for customer engagement: PwC
Summary
PwC has developed a patent-pending architecture for agentic customer engagement, designed to accelerate enterprises from AI experimentation to measurable business value. This architecture integrates advanced AI capabilities with common context, knowledge intelligence, real-time APIs, and enterprise workflow systems. Unlike solutions focused solely on prompt optimization, PwC's approach tackles deeper structural barriers such as fragmented context, orchestration gaps, latency constraints, and disconnected workflows. At its core is a shared cognitive layer that supports diverse AI ecosystems, including large language models, multimodal models, and computer vision, enabling true multimodal fusion from various data sources. The system separates engagement and action orchestration for improved interoperability and governance. Clients reportedly achieve 30–60%+ reduction in cost-to-serve, 2–5% revenue uplift, and 10–15 basis point improvement in CSAT/NPS. The architecture is model-agnostic and extensible across enterprise functions.
Key takeaway
For AI Architects or Directors of AI/ML seeking to move beyond isolated AI experiments to enterprise-wide impact, consider adopting an agentic AI architecture that integrates diverse AI capabilities with core enterprise systems. Your strategy should prioritize addressing structural barriers like fragmented context and latency, rather than just optimizing prompts. Focus on model-agnostic designs and separating orchestration layers to ensure scalability, governance, and future readiness for evolving AI services.
Key insights
PwC's patent-pending agentic AI architecture integrates diverse AI and enterprise systems to deliver measurable customer engagement value at scale.
Principles
- AI architecture should address structural barriers, not just prompt optimization.
- Separate engagement and action orchestration for better governance.
- Design for model-agnosticism and extensibility across enterprise functions.
Method
The architecture establishes a shared cognitive layer across AI ecosystems, integrating real-time APIs and enterprise workflows. It uses low-latency agentic data foundations for retrieval and memory, separating orchestration layers.
In practice
- Implement a shared cognitive layer for multimodal AI fusion.
- Design for real-time data retrieval and persistent memory.
- Structure AI for model-agnostic interoperability.
Topics
- Agentic AI
- Customer Engagement
- Enterprise AI Architecture
- Multimodal AI
- Workflow Orchestration
- AI Governance
Best for: Executive, AI Product Manager, Product Manager, Director of AI/ML, AI Architect, Consultant
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Editorial summary, takeaway, and curation by AIssential. Original article published by Curated for you: AI: PwC.