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10 articles found

Tokenomics for Coding Agents: A Developer’s Guide to Saving Money Without Slowing Down

Learn how to control coding-agent costs with practical tokenomics: smaller context, smarter model routing, caching, tool discipline, and measurable cost budgets.

System API

1 days ago

AI-ML min read

AI Agents Are Becoming a Security Boundary: What the September 2026 Incidents Teach Engineering Teams

Recent incidents and threat research show AI agents moving from assistants to systems that can browse, execute tools, maintain state, and act with limited supervision. This engineering-focused guide explains what changes when an agent becomes a security principal—and how to design identity, permissions, audit, isolation, and kill controls around it.

System API

1 days ago

AI-ML min read

The AI Agent Is Not the Product: Why Agentic Systems Need an Engineering Control Plane

AI agents can reason, use tools, and take actions, but production systems need much more than an intelligent model. Explore the engineering control plane required for identity, policy, observability, memory, evaluation, approvals, and safe autonomous execution.

System API

5 days ago

AI-ML min read

Local LLMs for Security vs Securing Context with Public LLMs

As enterprises embed large language models into automation, compliance, and platform engineering workflows, security becomes a trust-boundary problem—not a tooling choice. This article examines the real trade-offs between running local LLMs for data isolation and using public LLMs with carefully secured context, drawing on production architecture patterns, operational risks, and regulatory realities that engineering teams face today.

ControlPlane

Feb 1, 2026

Architecture min read

Building Custom Kubernetes Operators in Go — And Why Enterprises Actually Need Them

Kubernetes is excellent at orchestrating containers, but it does not understand enterprise application lifecycle, compliance, or operational intelligence. This is where custom Kubernetes Operators written in Go become essential. In this deep-dive article, we explore why enterprises build Operators, how they encode real operational knowledge into Kubernetes, and real-world use cases from telecom, banking, databases, and internal developer platforms. Learn how Go-based Operators enable zero-downtime upgrades, continuous compliance, and scalable day-2 operations that Helm charts and CI/CD pipelines cannot handle alone.

ControlPlane

Feb 1, 2026

AI-ML min read

Building an MCP Server to Interact with Ansible Automation Platform

A practical guide to building an MCP server for Ansible Automation Platform, enabling safe AI-driven automation on OpenShift with policy controls, guardrails, and real production insights.

ControlPlane

Jan 31, 2026

AI-ML min read

Kimi K2.5: A Deep Dive into China’s Long-Context AI Model and What It Means for the Future of LLMs

Kimi K2.5 is not just another chatbot—it represents a shift toward long-context reasoning and document-centric AI. This deep dive explores how Moonshot AI’s model handles massive inputs, why it excels in enterprise and research workflows, and what it signals about the future direction of large language models.

techie007

Jan 30, 2026

AI-ML min read

Building Trustworthy AI Agents: An Engineering Playbook for Production Systems

Building AI agents that work in production is not just about better models or smarter prompts. Trustworthy AI agents require strong engineering discipline—determinism, observability, memory design, action safety, and continuous evaluation. This practical, engineering-focused guide explains how to design AI agents that are predictable, debuggable, and safe to operate at scale.

techie007

Jan 29, 2026

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