Agentic SRE should earn authority one capability at a time. Human owners decide the limits; SRE brings the operational evidence.
A policy file describes intent, not what happened in a live run. Follow Bernstein’s evidence paths to see what reaches the live route, what gets recorded, and what still requires human judgment.
Rules work until messy humans touch them, and generative models ramble when code needs an exact answer. Jev and Laya turn that gap into a measurable, governable component.
Before an agent spends against a paid API, rehearse the workload locally. This Coffee Finder demo shows the cost, evidence, and limits leaders need to see.
Coding models are not software factories. Repository context, bounded authority, verification, recovery, and feedback turn generated code into trustworthy change.
When coding agents accelerate implementation, QA must move upstream. Put quality professionals at the center of intent, authority, evaluation, and learning.
ScaleKit tested MCP against command-line interfaces across 75 benchmark runs. MCP consumed 4–32× more tokens. Agent Skills offer a better standard.
Multi-agent systems solved input context management. Now the bottleneck is output. The artifact pyramid brings progressive disclosure to what agents produce.
