Manoj Saxena

AI Summary

→ WHAT IT COVERS Manoj Saxena, CEO of Trustwise, explains why 95% of AI agent projects fail to reach production and presents the case for a dedicated runtime control layer — called an AI control tower — that governs multi-agent systems across safety, compliance, and cost dimensions simultaneously, drawing on deployments at banks, insurers, and global brands. → KEY INSIGHTS - **The 95% Production Gap:** MIT research confirms that 95% of AI agent projects stall between pilot and production. The bottleneck is not building agents — frameworks make that fast — but satisfying risk, compliance, security, and finance requirements simultaneously. Trustwise addresses this by compressing the production-readiness timeline, functioning as what Saxena calls a "spell checker for trust" applied before deployment. - **Runtime vs. Declarative Governance:** Existing enterprise software — security, governance, and observability tools — each address only one dimension and none enforce policy at the moment of action. Runtime control means decisions happen within 10 to 300 milliseconds during agent execution, not in pre-deployment documents or post-hoc audit logs. Enterprises need a fourth category of infrastructure that combines all three postures simultaneously. - **Token Cost Explosion in Agentic Systems:** A single user input to an agentic system can trigger 20 to 50 downstream actions and consume 20 to 40 times more tokens than equivalent generative AI workflows from two years ago. Trustwise demonstrates up to 83% cost reduction alongside 40% safety improvement and 60% latency reduction by classifying workloads, simulating pipeline configurations, and dynamically routing models at runtime. - **Six-Layer Alignment Framework:** Effective agent alignment requires enforcement across six stacked layers: global human rights standards, national AI regulations (EU AI Act, Singapore, US), industry rules (FINRA, HIPAA, UKFCA), corporate values, business-unit-specific policies, and individual customer SLA requirements. Trustwise evaluates all six layers within each 10-to-300-millisecond runtime check, steering agent actions before they execute rather than flagging violations afterward. - **1,100+ Prebuilt Regulatory Controls:** Trustwise ships a library of over 1,100 runtime controls spanning 17 regulatory frameworks — including NIST AI RMF, EU AI Act, OWASP, HIPAA, SR 11-7, and UKFCA Consumer Duty — updated every 30 days. Enterprises can deploy these out-of-the-box policies immediately and extend them using a custom policy authoring tool, with services partners including Accenture, KPMG, and Hitachi Digital Services available for bespoke implementations. - **Semantic Action Layer as Core IP:** Rather than relying on knowledge graphs or context graphs, Trustwise built a proprietary "semantic action layer" that defines permissible action paths for each agent at runtime across all alignment layers. This data structure makes agent behavior deterministic and auditable, enabling reconstruction of every decision — model used, data accessed, policies applied — for regulatory defense, functioning analogously to a pre-computed navigation map rather than real-time route recalculation. → NOTABLE MOMENT For the first time on record, AI agent traffic on the internet surpassed human-generated traffic last month. Saxena compares this inflection point to when data traffic overtook voice calls on AT&T's network — a structural shift signaling that within three years, agents, not humans, will be the primary consumers of AI control infrastructure. 💼 SPONSORS None detected 🏷️ AI Governance, Agentic AI, Runtime Control, Enterprise AI Deployment, AI Compliance, AI Cost Optimization