From Demo Bots to Production Agents: Sandbox and Harness Controls in the 2026 SDK Era
A practical architecture for deploying long-horizon enterprise agents with isolation, tool boundaries, and measurable reliability.
A practical architecture for deploying long-horizon enterprise agents with isolation, tool boundaries, and measurable reliability.
How to adopt Cloud Run Worker Pools GA with queue design, SLOs, and cost-aware autoscaling in production.
How to redesign flaky pipelines, incident response, and AI-driven retries after GitHub introduced rerun limits.
A practical rollout guide for programmable flow protection on global networks, including safety controls, test harnesses, and incident runbooks.
A practical architecture for teams defending proprietary UDP protocols with programmable flow logic and staged safety controls.
How platform teams can adopt Cloudflare's new programmable mitigation model without breaking game, IoT, or proprietary realtime traffic.
Turning a one-line Kubernetes storage permission tweak into a repeatable reliability and cost optimization practice.
How to operationalize @copilot-driven PR edits and merge-conflict resolution with policy gates, auditability, and rollback discipline.
How to prepare Kubernetes platforms for inference-heavy workloads with durable agent orchestration, GPU scheduling, and reliability guardrails.
A production model for sandbox policy, observability, and rollback when running AI-generated code in Dynamic Workers.
Building layered egress controls that limit DDoS-amplified cloud costs while preserving service continuity and incident response speed.
How to reduce pod restart latency and protect rollout SLOs by applying fsGroupChangePolicy intentionally in Kubernetes production clusters.
Dynamic Workers and Workers AI updates suggest a new edge-agent runtime model. Here is how to adopt it with SRE, security, and FinOps discipline.
A practical playbook for reducing Kubernetes restart delays caused by storage permission scans in stateful platform workloads.
How to adopt Cloudflare’s dynamic worker sandbox approach for AI agents with policy isolation, deterministic tooling, and SRE-grade observability.
How platform teams should model capacity, thermal limits, and failure domains when moving to high-core edge generations.
How to keep velocity high while controlling risk when AI coding agents dramatically increase pull request volume.
How to adopt large-model inference on Cloudflare Workers AI with reliability budgets, latency strategy, and unit economics governance.
What engineering leaders can learn from stair-capable delivery robots: safety envelopes, fallback loops, and observability for real-world autonomy.
What engineering leaders can learn from large robotaxi funding rounds: reliability economics, safety SLOs, and city-by-city rollout control.
A pragmatic response plan after GitHub paused minimum version enforcement for self-hosted runners, balancing security hygiene and delivery stability.
A practical runbook for validating replication lag, failover timing, and application behavior in managed Valkey global setups.
How to design, execute, and institutionalize cross-region disaster recovery drills with Valkey Global Datastore and service-level cache contracts.
How rail, utility, and industrial operators can shorten recovery time with AI-assisted inspection and dispatch workflows.
How to respond to parser-level request smuggling issues in modern reverse proxies without breaking production traffic.
A practical operations playbook for combining parser hardening, stateful API scanning, and incident telemetry.
How network and platform teams can reduce silent packet loss and improve remote user experience with adaptive MTU and QUIC-first transport.
As AI inference shifts from periodic workloads to continuous traffic, organizations need new capacity models spanning edge, backbone, and application layers.
Cloudflare’s Dynamic Path MTU Discovery update highlights a wider reality: AI-era remote work depends on transport-layer resilience.