OpenAI, Anthropic, and Bloomberg presentations at AI Engineer New York focused on how financial agents can balance autonomy with safety through control layers and human review. One concrete point was Two Sigma’s use of auto-mode classifiers to decide routine calls and route only the most important requests to people. Financial firms are shifting toward sandboxed identities, deterministic external checks, audit trails, and approval boundaries so agents can delegate more work while limiting risky actions.
Google released updates on its Geospatial Reasoning agent under Earth AI, including a new technical report and expanded experimental access for testing and development. In early partner work during the 2026 Ebola outbreak in the DRC, the agent helped WHO AFRO’s team pinpoint 48 exposed settlements. The release adds evaluated geospatial reasoning across four benchmarks, new researcher benchmarks, and early access to Ask Google Earth deep analysis plus an agent-to-agent API for custom workflows.
A joint IDC and Lenovo study found that 88% of AI proof-of-concept projects fail to reach production due to unclear objectives, unprepared data, and limited in-house agentic AI expertise.
The study highlights the build-vs.-run gap, where agent designers and deployers often cannot run the agents inside business processes.
As a result, teams are urged to add production readiness practices like logging and timeouts, enforce least-privilege tool access, improve monitoring and runbooks, and validate agents again after environment changes.
Duvo, an enterprise AI automation startup, raised a $32 million Series A led by Balderton Capital to address stalled “agentic” AI pilots. The round came just 10 months after its $15 million seed. The funding will be used to expand into the US with retailers and consumer goods providers and to push its Enterprise Execution OS approach toward faster, more reliable deployment.
Evos raised $9 million in pre-seed funding to build an agentic operations platform that creates autonomous workers by having agents watch experienced employees and then perform tasks under human approval gates. After 24 hours of learning, an average agent can reach about 82% decision accuracy and handle around 70% of a human’s daily workload autonomously. Evos says the approach will let small industrial firms transfer hard-to-replace know-how into working automation and expand its Workhorse platform rollout later this year.
Liquid AI released the open-weight reasoning model LFM2.5-24B-A2B for running agent tool-use loops on laptops and PCs. The model is 24B parameters total with about 2.45B active parameters per token and supports a 131,072-token context. It expands the LFM2.5 line with longer context and a larger tokenizer, adds day-one local deployment formats and benchmarked tool-calling improvements, while weakening knowledge/coding unless paired with retrieval.
The EU’s next chapter in AI is being framed around the growing adoption of agentic AI by enterprises and small businesses. The analysis says this growth is occurring across EU enterprises and small businesses. It argues that deeper agentic use could unlock more value for these organizations.
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