Techstrong TV March 11, 2026

Why It Matters

These innovations directly reduce infrastructure spend, improve reliability, and address regulatory pressures around AI autonomy, positioning early adopters for competitive advantage.

Key Takeaways

• Sawmills cuts observability data by up to 70%.
• Nokia's SR Linux enables automated NetOps with drift control.
• Twilio's A2H protocol mandates human consent for AI actions.
• Microsoft Power Platform unifies agents, apps, and interfaces.
• Trustworthy AI needs security controls and human‑in‑the‑loop.

Pulse Analysis

The surge of AI‑generated code has turned observability into a data‑intensive challenge, inflating telemetry streams beyond traditional limits. Sawmills' agentic telemetry platform moves observability left, filtering and compressing signals at the source. Early benchmarks show up to a 70 % cut in raw data, lowering storage costs and speeding incident detection. By embedding policy‑driven sampling in agents, SRE teams can focus on high‑value alerts rather than data wrangling. The solution also plugs into existing observability stacks, enabling seamless migration without compromising service level objectives.

Customers report faster mean‑time‑to‑detect, improving overall system resilience. Nokia demonstrated how SR Linux paired with event‑driven automation replaces manual CLI tasks with true NetOps, offering declarative intent, drift detection, and automated remediation for data‑center fleets. Twilio’s open‑source A2H protocol adds a verifiable consent layer, requiring AI agents to obtain explicit human approval before privileged actions—critical for regulated sectors like finance and healthcare. Automation reduces mean‑time‑to‑repair by auto‑identifying root causes, freeing engineers for strategic work. The combined stack also provides audit logs that satisfy compliance auditors. These tools signal an industry shift toward transparent, auditable AI‑driven operations.

Microsoft’s Power Platform aims to fuse apps, agents, and interfaces under a low‑code umbrella, enabling rapid deployment of intelligent solutions while preserving centralized governance. Experts warned that scaling agentic workloads demands operational security controls, verifiable reasoning, and calibrated human‑in‑the‑loop oversight to close the AI trust gap. Enterprises that master this integrated approach can accelerate digital transformation, positioning themselves ahead of competitors in AI‑enabled service delivery. Such governance frameworks are becoming a prerequisite for enterprise AI procurement. Early adopters that embed these safeguards can reduce compliance risk and unlock new revenue from trustworthy AI services.

Original Description

Taming the Observability Data Explosion: Ronit Belson and Erez Rusovsky, co-founders of Sawmills, introduce an agentic telemetry platform designed to shift observability left—reducing runaway data volumes and controlling observability costs as AI-generated code accelerates software development.

From CLI to NetOps: Ahmed Abutaleb explains how Nokia modernized its data centers using Nokia SR Linux and Nokia Event-Driven Automation, moving from manual CLI-driven operations to automated NetOps practices with stronger traceability and drift control.

Standardizing the Agent-to-Human Connection: Zachary Hanif of Twilio discusses the new A2H (Agent-to-Human) open-source protocol, designed to ensure AI agents request verifiable human consent before executing actions in regulated environments.

Future of Agents & Apps: Ryan Cunningham and Daniel Newman outline Microsoft’s vision for connecting apps, agents and interfaces through Microsoft Power Platform, enabling organizations to build and govern intelligent applications at scale.

Agents of Dev Podcast Ep. 13: A deep dive into how development teams are adapting tooling, architecture and governance to manage AI-driven software development and agentic workflows.

Closing the AI Trust Gap: Experts examine the conditions required to deploy trustworthy AI systems—including operationalized security controls, verifiable reasoning, and carefully calibrated human-in-the-loop oversight for agentic workload