Agent context layers: Enterprises governing their AI data are catching twice as many bad answers as the ones who aren't

Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]

The Agentic Reckoning: Enterprise AI organizations have a runtime problem, not a model problem — and most are building the wrong solution

In Q1 2026, VentureBeat's Pulse Research surfaced the “Governance Mirage”: the gap between the governance org charts enterprises had drawn and the control layers they had actually built. Fort [...]

The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway

Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their interna [...]

Resolve AI says the AI coding boom is breaking production systems. It wants to fix that.

Resolve AI, the production-operations startup backed by Greylock and Lightspeed Venture Partners, today announced a sweeping expansion of its platform that introduces always-on background agents, a re [...]

Monitoring LLM behavior: Drift, retries, and refusal patterns

The stochastic challengeTraditional software is predictable: Input A plus function B always equals output C. This determinism allows engineers to develop robust tests. On the other hand, generative AI [...]

Fiduciary AI: Agents need to prove trustworthiness, not just ability

Presented by VijilIn dynamic environments where users, data, workflows and attack techniques change continuously after deployment, AI agent trust has become a runtime problem. Most organizations still [...]

How Deductive AI saved DoorDash 1,000 engineering hours by automating software debugging

As software systems grow more complex and AI tools generate code faster than ever, a fundamental problem is getting worse: Engineers are drowning in debugging work, spending up to half their time hunt [...]

85% of companies burned by an AI mistake are racing to cut the humans who might catch the next one

Enterprises that already got burned by an AI agent passing its evals and then failing in production are moving faster toward removing humans from deployment decisions, not slower — even as trust in [...]

AI agents are quietly generating chaos engineering failures enterprises don’t track yet

There is a category of production incident that engineering teams are not tracking yet — because it doesn't fit any existing postmortem template. The agent initiated an action. The action was t [...]