EU AI Act

What Is an Agent Scanner for GitHub AI Agent Detection? An agent scanner for GitHub AI agent detection is a security tool that scans repositories to identify, inventory, and assess the security posture of autonomous AI agents, their skills, MCP server configurations, and tool permissions. These scanners emerged rapidly in 2026 after the ClawHavoc campaign planted over 1,200 malicious skills into agent marketplaces, and the market has since grown to over 95 open-source tools on GitHub. They serve as the first line of defense for organizations adopting AI agents at scale, enabling teams to detect shadow AI, enforce compliance with regulations like the EU AI Act, and prevent supply-chain attacks on agent ecosystems. ...

Agentic coding tasks consume up to 1,000× more tokens than equivalent chat sessions, input tokens (not output) dominate the bill even with prompt caching enabled, and the same task can vary by 30× in total token consumption across runs with no correlation to output quality — yet most teams still have zero per-agent, per-feature cost attribution. Fixing that starts with tagging every LLM request at the SDK call site. Why Token Cost Attribution Matters More in 2026 Than Ever Enterprise GenAI spend hit $37B in 2025, up from $11.5B the year before. 80% of companies exceeded their AI cost forecasts by 25% or more (full breakdown). The old approach — a single API key for the whole team, one line item on the AWS bill — no longer works when agents are running unsupervised loops that burn 25,000–35,000 tokens per turn across 50-turn sessions. ...

The EU AI Act imposes legally binding obligations on developers and deployers of AI systems in the EU, with the primary enforcement deadline of August 2, 2026. However, the AI Omnibus deal reached in May 2026 significantly changed which requirements apply on that date — extending certain Annex III high-risk AI system deadlines to December 2027. This guide tells you exactly what still hits in August 2026, what got delayed, and the specific technical steps engineering teams must take now. ...

The AI governance market is on track to reach $9.2 billion by 2026 at a 25% compound annual growth rate, and 87% of enterprises will require formal AI agent governance frameworks by year end. The pressure is no longer hypothetical: autonomous agents that call APIs, write to databases, send external messages, and spawn sub-agents are in production across every regulated industry, and the window for treating governance as a future concern has closed. This guide covers the full governance stack — from regulatory mapping to RBAC design, audit logging specifications, zero-trust credential architecture, model versioning controls, and incident response playbooks — with enough operational specificity to move from awareness to implementation. ...

AI agents — systems that autonomously execute multi-step tasks, call external APIs, edit files, send messages, and invoke downstream agents — have moved from research prototypes to production workloads inside enterprise environments faster than governance structures can accommodate. The regulatory response has been equally rapid: AI legislation has increased 21.3% across 75 countries since 2023, representing a ninefold growth since 2016. US federal agencies alone issued 59 AI regulations in 2024, double the 2023 count, and approximately 700 AI bills were introduced across 45 US states in 2024 — up from 191 the prior year. Boards, legal teams, and CISOs who treated AI governance as a future problem now face present-tense regulatory exposure. This guide provides the frameworks, compliance mappings, and implementation steps required to govern AI agents at enterprise scale in 2026. ...