Build, discover, and monetize AI agents.

New tools to govern and secure AI agents in enterprise workflows

What changed: Codenotary launched AgentMon 3, an enterprise AI security platform that learns from AI agent behavior to adapt runtime security policies as agents operate across an organization. Automox released MCP Server 2.2, extending its governed agentic interface for endpoint operations with interactive review surfaces, patch-by-severity policies, and live capability discovery over its console and webhooks APIs. First Recon AI introduced its AI Security Runtime, which inspects every AI interaction—including human-to-model, agent-to-tool, and agent-to-agent—applying policy inline and recording decisions as audit-ready evidence. Attestiv’s new DeepScan platform automatically validates submitted files in business workflows, shifting from simple deepfake detection to trust assessment in context.

Why it matters: These launches signal a fast-maturing ecosystem for governing AI agents, giving teams security guardrails, review workflows, and compliance-ready logs without having to build their own governance stack. Founders and operators can move faster on agent deployments while satisfying security and audit demands from CISOs and regulators.

Try/watch: Map your current and planned AI agent use cases to these categories—runtime policy learning, governed endpoint operations, interaction-level inspection, and workflow file validation—and pilot at least one governance layer before scaling agents beyond a single team.

Abrigo rolls out agentic lending platform for banks

What changed: Abrigo announced a data-driven agentic lending platform that uses AI agents to help financial institutions scale lending operations with greater speed, consistency, and governance. The platform is positioned as an extension of Abrigo’s banking AI capabilities, focusing on automating parts of credit analysis and decisioning while maintaining controls required in regulated environments.

Why it matters: Community and regional banks often lack the engineering capacity to build custom AI agents, but they still face pressure to modernize lending workflows. A packaged agentic platform can cut underwriting cycle times and reduce manual review, while keeping decisions traceable for regulators and internal risk teams.

Try/watch: If you operate in financial services, start by identifying low-complexity lending tasks—document checks, data gathering, preliminary scoring—that can be handed to agents, and insist on clear audit trails and override controls in any vendor evaluation.

Benchmarks highlight which computer-use agents actually work

What changed: Coasty.ai published a detailed 2026 AI agent platform comparison focused on computer-use agents from OpenAI, Anthropic, UiPath, and Coasty itself. On the OSWorld benchmark for computer-use agents, Coasty’s in-house model reportedly scored 85.6% accuracy in internal tests and 82.81% on the public leaderboard, beating competing platforms in this category. The piece also catalogues failure modes and strengths of each vendor, arguing that many marketed capabilities underperform in real desktop-style tasks.

Why it matters: Builders relying on agents to operate software via a virtual computer need hard data, not marketing claims. Benchmark results like OSWorld’s help teams choose platforms that can reliably click through interfaces, fill forms, and execute workflows without constant human correction.

Try/watch: Before standardizing on any computer-use agent, run your own OSWorld-style test using a representative set of apps—CRM, billing, internal tools—and compare success rates between vendors against the tasks your business actually cares about.