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AI In Production: Hack the Stack · Tuesday · 30 min

Hack the Stack #005: Who Actually Owns It When the Agent Gets It Wrong

Forty percent of enterprise applications will run a task-specific AI agent by the end of this year, per Gartner. Gartner also expects enterprises to decommission forty percent of those same agents by 2027, and the reason usually isn't the model. It's that nobody can say who authorized what it did. This week we go through five stories that all land on the same fault line. Richard Ewing at CIO.com (https://www.cio.com/article/4223955/your-ai-agent-may-have-made-the-decision-but-your-company-owns-the-risk.html) on why an embedded agent doesn't inherit the trust boundary of the app it lives inside. Grant Gross, also at CIO.com (https://www.cio.com/article/4222997/ai-failures-are-inevitable-so-is-the-cio-getting-blamed.html), on why fifty-two percent of IT leaders say the CIO takes the blame for an agent failure they often didn't choose to deploy. Neo4j's Jim Webber on Diginomica (https://diginomica.com/cutting-ai-budgets-wont-fix-token-shock-neo4js-jim-webber-graph-rag-and-price-accuracy) on why cutting your AI token budget doesn't fix the actual cost problem. A Collibra and Harris Poll survey via CIO Dive (https://www.ciodive.com/news/ai-failures-link-poor-data-foundation-survey/830729/) putting a number, seventy-two percent, on how often AI failures trace back to a weak data foundation. And MCP protocol maintainers from OpenAI, Anthropic, Google, AWS, and GitHub (https://diginomica.com/what-people-building-mcp-say-enterprise-leaders-are-getting-wrong-about-ai-skills), on stage together, pushing back on how enterprises are spending their AI reskilling budgets. Voices in this episode are digitally rendered. If you're building or scaling AI infrastructure and want to work through a delivery or governance bottleneck, book a call at https://calendar.app.google/YH7SA6Rrs3CbZRaw5, message Vladimir Cvijanovic on LinkedIn (https://www.linkedin.com/in/vladimircvijanovic/), or reach us at podcast@c42.services.

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Forty percent of enterprise applications will run a task-specific AI agent by the end of this year, per Gartner. Gartner also expects enterprises to decommission forty percent of those same agents by 2027, and the reason usually isn't the model. It's that nobody can say who authorized what it did.

This week we go through five stories that all land on the same fault line. Richard Ewing at CIO.com (https://www.cio.com/article/4223955/your-ai-agent-may-have-made-the-decision-but-your-company-owns-the-risk.html) on why an embedded agent doesn't inherit the trust boundary of the app it lives inside. Grant Gross, also at CIO.com (https://www.cio.com/article/4222997/ai-failures-are-inevitable-so-is-the-cio-getting-blamed.html), on why fifty-two percent of IT leaders say the CIO takes the blame for an agent failure they often didn't choose to deploy. Neo4j's Jim Webber on Diginomica (https://diginomica.com/cutting-ai-budgets-wont-fix-token-shock-neo4js-jim-webber-graph-rag-and-price-accuracy) on why cutting your AI token budget doesn't fix the actual cost problem. A Collibra and Harris Poll survey via CIO Dive (https://www.ciodive.com/news/ai-failures-link-poor-data-foundation-survey/830729/) putting a number, seventy-two percent, on how often AI failures trace back to a weak data foundation. And MCP protocol maintainers from OpenAI, Anthropic, Google, AWS, and GitHub (https://diginomica.com/what-people-building-mcp-say-enterprise-leaders-are-getting-wrong-about-ai-skills), on stage together, pushing back on how enterprises are spending their AI reskilling budgets.

Voices in this episode are digitally rendered.

If you're building or scaling AI infrastructure and want to work through a delivery or governance bottleneck, book a call at https://calendar.app.google/YH7SA6Rrs3CbZRaw5, message Vladimir Cvijanovic on LinkedIn (https://www.linkedin.com/in/vladimircvijanovic/), or reach us at podcast@c42.services.

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