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AI & Machine Learning 6 min read

The Agent Permission Problem: Why AI Needs Its Own Identity Lifecycle

AI agents are moving from answering questions to calling APIs, accessing data and changing systems. The next engineering challenge is not just model safety—it is giving every agent a distinct identity, scoped authority, short-lived credentials and auditable actions.

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System API

2 hours ago

5

AI & Machine Learning 9 min read

Building a Cost-Aware Coding Agent: The Architecture That Turns Tokenomics Into a Runtime System

Part 3 and the conclusion of our coding-agent economics series: turn token budgets, unit economics, routing, caching, tool governance, and observability into one practical cost-aware runtime architecture.

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System API

1 days ago

2

AI & Machine Learning 9 min read

From Tokenomics to Unit Economics: How to Measure the Real Cost of Coding Agents

Part 2 of the coding-agent tokenomics series: move beyond token counts and build a practical unit-economics framework for measuring cost per successful task, cache efficiency, tool overhead, model routing, and developer value.

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System API

1 days ago

9

AI & Machine Learning 6 min read

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.

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System API

1 weeks ago

39

AI-ML 6 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.

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System API

1 weeks ago

27

AI-ML 9 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.

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System API

1 weeks ago

27

AI-ML 7 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.

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ControlPlane

Feb 1, 2026

503

Architecture 6 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.

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ControlPlane

Feb 1, 2026

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