As enterprise AI agents move into production, organizations are confronting a growing reliability problem. Many teams are discovering that LLM performance alone does not determine whether agents succeed in production. Long-running AI workflows must survive crashes, preserve state, recover from failures, manage inference costs, and coordinate across APIs, tools, and enterprise systems.

There is a category of production incident that engineering teams are not tracking yet — because it doesn't fit any existing postmortem template.

At the AI NOW Summit, held at a venue in central Paris, co-founder and CEO Arthur Mensch took the stage alongside CTO Timothée Lacroix and Chief Scientist Guillaume Lample to lay out a strategy that stretches from bare-metal GPU clusters to physics simulations for aircraft wings. The company disclosed that it now employs 1,000 people and is targeting €1 billion ($1.17B USD) in revenue for 2026 — a figure that, if achieved, would be an extraordinary growth trajectory for a company that began with 15 employees collaborating with its first customer, BNP Paribas, in 2023.

The centerpiece of the release is a new multi-agent investigation system developed by Resolve AI's in-house research lab. Instead of deploying a single AI agent to diagnose a production failure — analogous to a lone engineer pulling an on-call shift — the platform now dispatches a coordinated team of specialized agents that pursue multiple hypotheses in parallel, independently verify each other's conclusions, and construct complete causal chains from root cause to symptom. The company says the architecture delivers more than a twofold improvement in root cause accuracy on its internal evaluation benchmarks compared to earlier versions of its platform.

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The data processing agreement (DPA) — the bedrock contract companies use to evaluate how vendors handle personal data — can no longer be trusted at face value. That is the central, and arguably most alarming, conclusion of DataGrail's Privacy and AI Trends Report 2026, released today.

For months, the leading AI coding benchmarks have told enterprise buyers a comforting but misleading story: the top models are all roughly the same. OpenAI's GPT-5 family, Anthropic's Claude Opus, and Google's Gemini Pro have clustered within a narrow band on Scale AI's SWE-Bench Pro leaderboard, making it nearly impossible for engineering leaders to determine which agent will actually perform best inside their codebases.

At the AI NOW Summit, held at a venue in central Paris, co-founder and CEO Arthur Mensch took the stage alongside CTO Timothée Lacroix and Chief Scientist Guillaume Lample to lay out a strategy that stretches from bare-metal GPU clusters to physics simulations for aircraft wings. The company disclosed that it now employs 1,000 people and is targeting €1 billion ($1.17B USD) in revenue for 2026 — a figure that, if achieved, would be an extraordinary growth trajectory for a company that began with 15 employees collaborating with its first customer, BNP Paribas, in 2023.