Build, discover, and monetize AI agents.

Nimble rolls out expert-level web search agents for complex research

What changed: Nimble announced it will demo new expert-level web search agents at AI4 2026 in Las Vegas, showcasing autonomous agents that learn a user's domain to execute complex research and dataset-building workflows. The product combines web search, crawling, and enrichment agents and is exposed via API, SDK, and MCP so AI builders can plug live web intelligence into their own stacks.

Why it matters: For founders and product teams, Nimble's focus on self-learning, domain-specific agents offers a way to offload repetitive expert research tasks without having to build custom scraping and enrichment systems from scratch. Lower token costs and higher answer accuracy compared to general web search could make continuous competitive and market intelligence viable for much smaller teams.

Try/watch: If attending AI4, block time to watch Nimble's live demos and ask how its agents would handle your most complex recurring research flows, then test its API against a real internal project within the next month.

Enterprise agentic AI adoption surges, but security visibility lags badly

What changed: Snyk released Volume II of its State of Agentic AI Adoption report, finding that security teams typically see only about one-third of their organization's real AI footprint. The study, covering more than 3,000 enterprise accounts, reports that the share of organizations running agentic architecture has risen from 28% to 33% in six months, and among adopters, full-stack setups combining agent frameworks and MCP servers climbed from 36% to 50%.

Why it matters: Most enterprises are underestimating their AI attack surface by roughly a factor of three, meaning many agents, retrieval systems, and data pipelines are operating without formal security review or monitoring. For CISOs and engineering leaders, the numbers suggest agent inventories and threat models need to expand beyond LLM counts to cover orchestration layers, MCP endpoints, and supporting infrastructure.

Try/watch: Start by mapping every agent framework, MCP server, and retrieval system in production, then compare that inventory to what your security tools actually monitor to quantify the visibility gap.

Redpanda pushes out-of-band governance for agent stacks

What changed: Redpanda published a post introducing new governance capabilities in its Agentic Data Plane, designed so teams can see every agent, control what each one accesses and returns, and eventually stop any agent instantly. The update centers on an out-of-band policy engine at the MCP boundary rather than inside individual agents, letting policies enforce which systems agents can reach and what data can leave without relying on agent cooperation.

Why it matters: This shift to out-of-band governance gives operators a way to rein in agent sprawl and enforce security and compliance policies even when agents are built by different teams or vendors. For data platform owners, consolidating authorization, auditing, and kill switches at a shared control plane simplifies proving to auditors and customers that autonomous agents cannot bypass guardrails.

Try/watch: Evaluate whether your own agent stack has a centralized control layer; if not, pilot an out-of-band policy engine on a high-risk MCP boundary such as production databases or third-party APIs.