[
  {
    "title": "Why Static Interpretability Fails on Multi-Step Agentic Decision Chains",
    "slug": "static-interpretability-agentic-chains",
    "published": "2026-09-18",
    "html": "https://devencelab.com/articles/static-interpretability-agentic-chains",
    "markdown": "https://devencelab.com/articles/static-interpretability-agentic-chains.md",
    "category": "Interpretability",
    "summary": "A technical account of the mismatch between single-forward-pass interpretability methods and the failure modes of agents that plan, act, observe, and revise over many steps - grounded in published circuit-analysis literature, a controlled failure-attribution benchmark, and emerging graph-based tracing methods.",
    "author": "Devence Lab Research Unit",
    "reading_time": "14 min read",
    "featured": false
  },
  {
    "title": "The Agent Sandbox: A Reference Architecture for Isolating Autonomous AI Systems",
    "slug": "agent-sandbox-reference-architecture",
    "published": "2026-09-16",
    "html": "https://devencelab.com/articles/agent-sandbox-reference-architecture",
    "markdown": "https://devencelab.com/articles/agent-sandbox-reference-architecture.md",
    "category": "Assurance",
    "summary": "An industry-neutral engineering reference on containing agents that browse, execute code, and act on the open internet - synthesized from disclosed CVEs, published isolation-technology internals, OWASP's agentic risk taxonomy, and the operating architectures of production agent-sandbox vendors.",
    "author": "Devence Lab Research Unit",
    "reading_time": "36 min read",
    "featured": false
  },
  {
    "title": "Runtime Monitors for Autonomous Systems: Detecting Drift and Misbehavior After Deployment",
    "slug": "runtime-monitors-autonomous-systems",
    "published": "2026-09-14",
    "html": "https://devencelab.com/articles/runtime-monitors-autonomous-systems",
    "markdown": "https://devencelab.com/articles/runtime-monitors-autonomous-systems.md",
    "category": "Assurance",
    "summary": "A methodological deep dive into why evaluation is not oversight, how the runtime-verification and machine-learning-monitoring traditions converge on autonomous language-model agents, and what the published, quantitative record says about how well post-deployment monitors actually work.",
    "author": "Devence Lab Research Unit",
    "reading_time": "33 min read",
    "featured": true
  },
  {
    "title": "Case Study: Using Interpretability to Catch a Specific Failure Mode Before Deployment",
    "slug": "interpretability-catching-failure-modes",
    "published": "2026-09-12",
    "html": "https://devencelab.com/articles/interpretability-catching-failure-modes",
    "markdown": "https://devencelab.com/articles/interpretability-catching-failure-modes.md",
    "category": "Interpretability",
    "summary": "A technical breakdown of three verified, published research programs that used mechanistic interpretability, linear probes, sparse autoencoders, and residual-stream analysis, to detect deception, hidden reward-hacking objectives, and strategic underperformance in language models before those models reached real users.",
    "author": "Devence Lab Research Unit",
    "reading_time": "23 min read",
    "featured": false
  },
  {
    "title": "Sparse Autoencoders: What They Reveal, and the Accuracy Tradeoffs Nobody Advertises",
    "slug": "sparse-autoencoders-tradeoffs",
    "published": "2026-09-10",
    "html": "https://devencelab.com/articles/sparse-autoencoders-tradeoffs",
    "markdown": "https://devencelab.com/articles/sparse-autoencoders-tradeoffs.md",
    "category": "Interpretability",
    "summary": "A methodological deep dive into sparse dictionary learning for neural network interpretability, how sparse autoencoders (SAEs) work, what they have genuinely revealed, and the specific, measured costs that get quietly left out of the demo.",
    "author": "Devence Lab Research Unit",
    "reading_time": "24 min read",
    "featured": false
  },
  {
    "title": "Attribution Graphs: A Technical Walkthrough of Circuit Tracing, and Where It Breaks",
    "slug": "attribution-graphs-circuit-tracing",
    "published": "2026-09-08",
    "html": "https://devencelab.com/articles/attribution-graphs-circuit-tracing",
    "markdown": "https://devencelab.com/articles/attribution-graphs-circuit-tracing.md",
    "category": "Interpretability",
    "summary": "A methodological deep dive into how Anthropic's interpretability team traces the internal computation of large language models, what the resulting \"attribution graphs\" actually show, and the specific, documented ways the method fails.",
    "author": "Devence Lab Research Unit",
    "reading_time": "32 min read",
    "featured": false
  },
  {
    "title": "Chain-of-Thought Monitoring: A Fragile Window Into Model Cognition",
    "slug": "chain-of-thought-monitoring",
    "published": "2026-09-06",
    "html": "https://devencelab.com/articles/chain-of-thought-monitoring",
    "markdown": "https://devencelab.com/articles/chain-of-thought-monitoring.md",
    "category": "Assurance",
    "summary": "A technical deep dive for engineers and researchers building oversight into agentic systems",
    "author": "Devence Lab Research Unit",
    "reading_time": "18 min read",
    "featured": false
  }
]