Your agent dashboard is green. The run underneath it is where the work actually broke.

2026-06-07 · Source: Nate’s Substack · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, quick

Summary

The content highlights a fundamental shift in product behavior, moving from user sessions to "delegated work" facilitated by AI agents. This transition offers an unprecedented opportunity for real-time decision impact analysis and agent shaping. Historically, immediate feedback on decisions was unavailable, but agents now provide continuous runtime telemetry and observability. This capability allows developers to steer and refine agents "full speed in real time," a significant advancement over traditional development cycles. The speaker quantifies this productivity, noting an equivalent of "55,000 developer years" of code production in the last year, based on "10 billion tokens," underscoring the critical need for effective agent shaping mechanisms.

Key takeaway

For AI Engineers building agentic systems, your focus should shift from merely evaluating agent performance to actively shaping and steering agents in real time. Embrace runtime telemetry not as a burden, but as the primary mechanism to influence agent behavior and maximize productivity. Prioritize building robust feedback loops to capitalize on the unprecedented scale of delegated work, potentially equivalent to "55,000 developer years" of output.

Key insights

AI agents enable real-time shaping and steering of product behavior, shifting from session-based to delegated work.

Principles

• Real-time telemetry is an opportunity.
• Agent shaping is crucial for scale.
• Productivity scales with delegated work.

In practice

• Implement agent runtime telemetry.
• Focus on agent shaping mechanisms.
• Measure agent output in tokens.

Topics

• AI Agents
• Real-time Observability
• Agent Telemetry
• Delegated Work
• Product Behavior
• Developer Productivity

Best for: AI Architect, AI Product Manager, MLOps Engineer, AI Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Nate’s Substack.