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95% of AI Agent Projects Fail to Reach Production. Here's Why | Manoj Saxena, TrustWise

August 24, 2026

AI Summary

5 min read

In August 2024, for the first time, agent traffic on the internet exceeded human traffic. Manoj Saxena, CEO of TrustWise, calls this a milestone shift comparable to when data traffic overtook voice traffic on the AT&T network. The implication is stark: we are deploying a digital workforce at scale, but most companies lack the infrastructure to control it. Saxena’s career spans the IBM Watson era, building startups, and teaching responsible AI at UT Austin and Cambridge. When ChatGPT launched, he saw the industry focused on building bigger, more intelligent models—"bigger nuclear cores"—without anyone thinking about "putting a dome on top." That gap became TrustWise’s mission: runtime governance for autonomous AI systems.

Why trust is a runtime problem, not a policy document

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What you'll learn

  • 1 (00:00) **The Core Problem: Trust & Control in the Age of Autonomous Agents** - Manoj Saxena introduces the central issue: AI has moved from generating output to taking actions, creating a new risk surface where alignment to business intent is critical.
  • 2 (01:12) **Manoj Saxena’s Background: From IBM Watson to TrustWise** - Saxena shares his journey from commercializing the Jeopardy-winning Watson system to founding TrustWise, driven by the realization that intelligence without control is not deployable.
  • 3 (04:27) **Why Trust is Now a Critical, Three-Pronged Issue** - Saxena breaks down the three fundamental shifts that have made runtime governance an urgent necessity for enterprises.
  • 4 (06:24) **The AI Control Tower: An HR Department for Digital Workers** - Saxena introduces the concept of a new infrastructure layer that acts as a management and governance system for AI agents, analogous to HR and finance for human employees.
  • 5 (08:29) **Runtime Governance: The White Space in Enterprise Software** - Saxena explains why existing security, governance, and observability tools are fundamentally incapable of providing the runtime control that autonomous agents require.
  • 6 (10:04) **Real-World Example: The UK FCA Consumer Duty Law** - Saxena provides a concrete example of how runtime control must differentiate agent behavior based on context and regulation, a task no current system can perform.
  • 7 (11:48) **Modular AI Shields: The Superpower Pack for Guardian Agents** - Saxena differentiates Guardian agents from the modular "shields" they can be equipped with to manage different aspects of trust.

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Show Notes

It takes a few hours to build an AI agent. It takes six to seven months to get it into production. Manoj Saxena - the executive who commercialized IBM Watson - built TrustWise around one thesis: intelligence without control is not deployable. In this episode, he joins Craig Smith to explain why 95% of enterprise AI agent projects are stalling between pilot and production, and why the answer has nothing to do with the quality of the underlying models. The bottleneck, Saxena argues, is the absence of an entirely new class of infrastructure, something that can evaluate every tool call, every action, every output of every agent at runtime, in milliseconds, against the full stack of alignment requirements that govern what an AI is actually allowed to do inside a real enterprise.

TrustWise's AI Control Tower does that across all vendors and agent frameworks simultaneously, operating in live, sidecar, batch, or simulation mode and aligning agent behavior against six layers of requirements - from UN Human Rights frameworks down to individual customer SLA commitments - in 10 to 300 milliseconds per decision. The conversation covers demonstrated results (83% cost reduction, 40% safety improvement), the token consumption paradox that's making agentic AI far more expensive than expected even as token costs fall, and a milestone Saxena compares to the moment data traffic surpassed voice on AT&T's network: last month, for the first time ever, agent traffic on the internet exceeded human traffic. The episode closes with a preview of Genesis agents, TrustWise's next product, designed not just to prevent bad outcomes but to surface beneficial hypotheses by looking 95 moves deep into enterprise data, in domains like fraud detection and revenue leakage, in the way Deep Blue looked 95 moves deep in chess.

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