Tag: ai-agents

AI agents are compressing DevOps feedback loops from hours to seconds. The teams that win will be those that design tight, verifiable loops — not the ones with the best dashboards. Here is how to think about loops when your operators are autonomous.

A practical comparison of three frameworks for structuring AI-assisted development in OpenCode — spec-driven development with OpenSpec, agent-first workflows with BMAD Method, and context-engineered pipelines with GSD Core.

A practical comparison of memory architectures for AI coding assistants — file-based, vector, graph, and hybrid approaches — with specific recommendations for opencode users.

Hands-on guide to running OpenSpec's spec-driven development workflow inside OpenCode — with real examples from an actual Next.js project including delta specs, OPSX protocol setup, and the plan-delegate-archive cycle.

A technical comparison of STATE-Bench, AMA-Bench, GroupMemBench, MemoryArena, and EvoMemBench — five benchmarks proving AI agents cannot remember and the graph architectures that might fix it.

Empirical analysis of token consumption in LLM-based multi-agent systems reveals that 59.4% of tokens go to code review, not generation — and a 2:1 input-to-output ratio exposes the 'communication tax' haunting agentic workflows.

Comparing the five major approaches to building agentic AI workflows — when to use monolithic frameworks, multi-agent orchestration, or the emerging LLM router pattern for autonomous tool selection.

Microsoft's DELEGATE-52 benchmark proves frontier models corrupt documents beyond 20 interactions. One week later, Google confirmed criminals used AI for a real zero-day exploit. The two findings describe the same gap from opposite ends.

How CoreCoder reverse-engineered Anthropic's Claude Code from 512K lines into a minimal 950-line implementation, revealing the essential architecture of modern AI coding agents.

The failure modes that plague distributed systems appear identically in multi-agent AI teams: stale locks, split brain, cascade failures, and Byzantine faults. The solutions are decades old.

ACP standardises how editors talk to coding agents. Here's how it works, who supports it, and how to orchestrate 90+ projects with a single CLI.

AWS has taken two specialised AI agents from preview to general availability. One keeps your systems running, the other breaks into them. Both are available today.

Kubescape 4.0 brings eBPF-based runtime threat detection to general availability, adds AI agent security scanning for KAgent workloads, and removes the high-privilege host-sensor DaemonSet entirely.

How Peter Steinberger's personal AI assistant went from 500 stars to 358,000, what the architecture looks like, and why OpenAI hired its creator.

A practical guide to engineering prompts for autonomous AI systems that plan, act, and iterate toward goals.

Combine n8n's workflow automation with NVIDIA GB10 Grace Blackwell hardware for privacy-preserving, high-performance AI automation. Real-world use cases and implementation guide.