Google Cloud Agent Cuts Engineer Onboarding Time 3x

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Google Cloud Agent Cuts Engineer Onboarding Time 3x

Google Cloud and Randstad Digital deployed an agentic AI solution called Forze Mirate for Forze Hydrogen Racing, a student team building hydrogen-powered race cars. Built with Gemini Enterprise and Gemini models, the agent lets new engineers query 18 years of technical history, including CAD drawings, telemetry, specs, safety guidance, and alumni knowledge.

Forze faces 100% annual workforce turnover, onboarding 50–60 new student engineers each year. Google Cloud says the agent reduced time-to-productivity by up to 3x, reduced unnecessary reliance on alumni and lead engineers by 80%, and improved institutional knowledge retention by 50%.

Strategic signal: knowledge-transfer agents are moving from abstract enterprise demos into measurable operational workflows.

IBM + ServiceNow Build the Enterprise Agent Data Layer

IBM and ServiceNow expanded their collaboration to help large enterprises modernize legacy systems, unlock AI-ready data, and deploy agentic AI across core operations. The companies said the partnership will combine IBM’s AI, automation, watsonx.data, Red Hat Ansible, Instana, Terraform, Vault, and modernization tools with the ServiceNow AI Platform.

The focus is on three bottlenecks: legacy application modernization, enterprise data governance, and autonomous infrastructure operations. Joint solutions are expected in the second half of 2026.

Strategic signal: enterprise agent adoption is becoming less about models and more about data fabric, workflow control, and legacy-system modernization.

Happiest Minds Launches Agentic SDLC Platform

Happiest Minds launched Rel(AI)Build, an enterprise-grade agentic AI platform for software delivery, alongside its Agentic Development Lifecycle methodology. The platform orchestrates specialized agents across planning, modernization, coding, testing, operations, cybersecurity, data engineering, and infrastructure workflows.

Happiest Minds says Rel(AI)Build includes governance, auditability, human-in-the-loop controls, shared contextual memory, and token-sensitive architecture to manage cost and performance. Early implementations reportedly showed 40–60% faster modernization timelines, 3x engineering productivity, and 30–50% lower support costs.

Strategic signal: software services firms are turning agentic delivery into packaged methodology, not just internal productivity tooling.

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PhoenixAI Raises $80M for the “Agentic AI Database”

PhoenixAI raised an $80 million Series B led by Sky9 Capital to build what it calls an “Agentic AI Database.” The company says AI agents create a new database workload because they issue unpredictable, real-time queries across live and historical data instead of relying on pre-modeled dashboards.

PhoenixAI says customers including AppLovin, Coinbase, Conductor, and Demandbase already use the platform in production. The company positions sub-second access to live enterprise data, governance, and deployment flexibility as requirements for production agent workloads.

Strategic signal: databases are being rebranded and rebuilt around agent query patterns, not just human analytics.

Coram Raises $35M for Autonomous Physical Security Agents

Coram AI raised $35 million in Series B funding to expand its AI-powered physical security platform. Its “Deep Investigation” agent lets users ask plain-language questions across months of video, access logs, visitor data, and hundreds of cameras, returning reports in minutes instead of hours.

The company now operates at more than 1,500 locations and has raised $66 million total. Coram says its edge AI approach can reduce cloud exposure, but the product also raises surveillance questions because it supports facial recognition, license-plate reading, tailgating detection, and gun detection.

Strategic signal: agents are moving into physical-world operations, where autonomy creates both efficiency gains and governance concerns.

Google DeepMind Puts $10M Behind Multi-Agent Safety

Google DeepMind, Schmidt Sciences, the Cooperative AI Foundation, ARIA, and Google.org announced a technical research funding call of up to $10 million for multi-agent AI safety. The program focuses on how millions of agents built by different organizations may communicate, negotiate, transact, and interact across shared digital environments.

Priority areas include sandboxes and testbeds, the science of agent networks, secure agent infrastructure, and oversight/control methods for deployed agent populations. The application deadline is August 8, 2026, with awards expected in autumn 2026.

Strategic signal: frontier safety work is shifting from single-model alignment to ecosystem-level risks among interacting agents.

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