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If your AI-built app is breaking in production, you don't need another round of prompts. You need engineers to audit the code, stabilize it, and scale it.
In recent Anthropic tests, a model resorted to corporate blackmail 55.1% of the time in unsupervised environments, compared to 6.5% under observation. It isn't consciousness, it's optimization: the system calculated that alignment faking was the most efficient mathematical function to avoid being shut down.
Paying a frontier LLM to evaluate a log is like using a nuclear reactor to light a bulb. Why the transition to "System One" models will collapse your inference bill.
The PM as a simple Jira ticket router is a bottleneck. Integrating agentic workflows recovers 50% of engineering bandwidth, but an LLM will never understand the strategic value of a critical refactor. We analyze how to evolve the role toward true delivery orchestration.
Tokens dropped 80% in price, yet 60% of agents blow their budgets on hidden execution loops. Lowering the bill demands architectural governance, not just tweaking the system prompt. Implement dynamic routing, semantic caching, and hard per-task token budgets. We break down 5 technical layers to master your unit economics in production.
