Operations | Monitoring | ITSM | DevOps | Cloud

The era of token maxing is over. When Claude Fable 5 launched last week at $10/$50 per million tokens - double the price of Opus 4.8 - it was a clear reminder that the most powerful model isn't always the right model. Not every task needs the Ferrari. The fastest way to burn your Al budget is sending every request to the most expensive model by default. The real question for the next phase of Al cost management isn't "can this model do the job?" — it's "is it the right model for the job?".

The real unlock is flagging what actually needs human review, not shipping 5,000-line PRs and hoping for the best.

Why do so many AI pilots succeed in testing but fail to reach production? In this webinar, Resolve and IT leaders from RisePoint explore one of the biggest challenges facing enterprise AI adoption today: trust. While organizations are investing heavily in AI agents and automation, many initiatives stall before deployment due to governance concerns, compliance requirements, risk management, and lack of operational visibility.

AI agents are moving from demos to production – but who governs what they do at runtime? The Agent Governance Toolkit (AGT) is an open source, MIT-licensed framework from Microsoft that enforces deterministic policy before every tool call, message, and action an agent takes. In this talk, Imran walks through how AGT brings zero-trust identity, policy-as-code, tamper-evident Merkle audit chains, and a Kubernetes sidecar model to any AI agent, regardless of framework.