Capgemini’s Agentic AI Engineering vs. ours

Capgemini's approach to Agentic AI Engineering reflects their broader delivery model: large teams, long timelines, and a scope that expands with the engagement rather than resolving it. iGate acquisition (2015) and Altran acquisition (2020) created chronic integration chaos — disparate cultures, overlapping offerings, unresolved quality standards

Agentic AI Engineering requires a specific kind of engineering precision that generalist delivery models do not produce. The capabilities required — Multi-agent orchestration architecture (LangGraph, AutoGen, CrewAI), Long-horizon planning and autonomous decision-making systems, RAG pipelines with enterprise knowledge bases — are not skills that scale with headcount. They require engineers who have delivered these systems in production environments.

Our Agentic AI Engineering practice deploys teams with production experience in the specific capabilities this service requires. Our agentic AI teams build systems that operate without human intervention loops — not demonstrations or prototypes. An agent we deploy for a healthcare client can triage inbound clinical requests, pull relevant patient history, cross-reference formulary data, and generate a compliant draft response — within HIPAA guardrails, with every action logged. The agent does not call a human for each step. It operates. We build the agent, the compliance layer, the monitoring, and the escalation logic. Then we leave. The system keeps running.

Fixed-price delivery with defined milestones. The first milestone is always a working system component — not a document. The engagement closes with full IP transfer: source code, documentation, and the operational capability for your team to run the system independently.

Compliance-Native Architecture Guide

Design principles and a structured checklist for building software that is compliant by default — not compliant by retrofit. For teams building in regulated industries.