Capabilities
Choose the failure mode your team recognizes. Each path starts with diagnosis, then the production substrate the inheriting team can operate.
AI agents and multi-agent harnesses
A successful tool call does not mean a successful task. Production agents need task-level success criteria, not only step-level telemetry.
Agents look fast in demos and still fail in production. Loops, invented tools, missing approval gates, and silent side effects show up the moment real users and real permissions arrive. The team that inherits the system gets the pager.
Production RAG applications
Retrieval quality can look excellent until users stop asking clean demo questions.
Retrieval quality looks excellent on demo questions and collapses when users ask messy, multi-hop, or stale-data questions. Hallucinations often start as retrieval misses, not model creativity. The platform team inherits the inconsistency.
Enterprise AI platforms
Access to a model is not the same as governance of AI behavior.
Enterprises buy model access and call it a platform. Without governance, routing, residency, and audit, internal AI becomes a pile of prompts nobody can explain under review. Platform owners inherit the risk.
Generative BI and intelligence layers
Natural-language access to data is dangerous when metric definitions are not controlled.
Natural-language access to data sounds useful until the model invents a metric, joins the wrong table, or answers fluently with the wrong number. Finance and ops inherit the distrust.
AI observability platforms
Low latency does not prove an AI system is healthy.
Latency is green while task success collapses. Teams cannot see why an agent looped, which tool failed, or when retrieval quality drifted. Operators inherit a black box.
FAQ
For concrete examples of how these areas show up in practice, read the reports.