Build, discover, and monetize AI agents.

Enterprise teams are overconfident about agent safety while controls lag

What changed: Harness published a survey-backed report showing a wide “confidence gap”: large organizations say they trust deployed AI agents but lack specific controls—for example, 77% say they have a complete inventory of agents while only 44% run active discovery tooling, and 74% trust testing to catch failures but just 19% have an automated gate to block bad releases.

Why it matters: If you build, buy, or run agentic workflows, this means many deployments are operating on faith rather than verifiable controls; undetected agents, weak rollout gates, and slow shutdowns create real production, security, and budget risk.

Try/watch: If you’re responsible for production agents, run two short checks this week: (1) run discovery to prove what agents and models are actually running, and (2) add a blocking gate or canary rollout for agent changes. If you’re a buyer, ask vendors for evidence of inventory, automated gates, and an auditable rollback path.

Fund Recs launches an “Agentic Platform” and AI Ops service for regulated finance teams

What changed: Fund Recs announced an Agentic Platform and a managed Fund Recs AI Ops service that puts specialized agents (support, document extraction, template builder, resolution and controls agents) inside its oversight layer and promises that client data never leaves the environment; the platform is built on the open Model Context Protocol (MCP) and is live with three production agents today.

Why it matters: For regulated businesses that can’t sacrifice auditability or data residency, this is an example of a vendor turning agentic automation into an auditable, human-supervised workflow — agents prepare work, humans review and sign off, and outputs feed deterministic rules when required. That pattern is a practical blueprint for compliance-minded adopters.

Try/watch: Pilot an “agent-as-preparer” use case (document extraction + human approval) rather than full automation. Require an audit trail and a human review step before any agent output becomes a control action. If Fund Recs is a vendor you evaluate, ask for logs showing agent decisions and how the MCP-based interface maps identity and permissions.

Splunk ties observability to security to speed incident response for agent-era attacks

What changed: Splunk published guidance showing how observability data (what’s actually running and healthy) should be combined with security detections so teams can triage AI-assisted or agentic attacks faster, and included a four-step integration checklist and required product versions for the workflow.

Why it matters: Agentic attacks compress timelines—threats can ripple across services in minutes—so teams need a single, evidence-rich incident view that shows whether suspicious activity reached running code and which service and owners are affected; that reduces noisy handoffs between security and ops.

Try/watch: For operators and security leads, prioritize a short integration sprint that brings runtime traces and service context into your security investigation queue. Test the end-to-end path (detection → service owner → remediation) with a tabletop exercise that simulates an agent-driven exploit. Monitor vendor guidance for patches and config specifics tied to agent-related detections.