System API

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.

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.

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.

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.

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.

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.