What Peer Evaluations Reveal About AI Agents in 2026

Security operations teams are deploying agents to own alert queues, incident response workflows are being rebuilt around AI-driven triage, customer-facing operations are running agents on live calls, and product teams are shipping new agentic capabilities into production.

On Sagetap, security, operations, and AI infrastructure leaders are deep into evaluations across these functions and more, and the requirements look very different depending on where agents are being pointed.

Why This Matters Beyond AI Agents

Deploying AI agents without the right infrastructure creates exposure across every function that depends on them, including:

• ‍AI Security & Governance: Agents acting on behalf of users need verified identities, scoped access, and audit trails. Without a governance layer, every agent deployed is a privileged actor with no accountability chain.‍
• Identity & Access Management: Agents are non-human identities that authenticate, delegate, and chain access across systems. Traditional IAM wasn't designed for workloads that operate continuously, autonomously, and across multiple environments simultaneously.‍
• Threat Detection & Response: AI agents generate behavior that existing detection tools weren't built to interpret. Security teams need new signal models to distinguish legitimate agent activity from compromised or misconfigured agent behavior.‍
• Data Protection & Privacy: Agents move data across systems as part of normal operation — summarizing, retrieving, writing, and passing context between tools. Each of those handoffs is a potential data exposure point that legacy DLP controls can't intercept.‍
• Vulnerability Management: AI coding agents accelerate software development but introduce vulnerable dependencies into supply chains at the same speed. Traditional VM tools scan what was deployed rather than what agents are actively generating.

Every function that routes work through an AI agent inherits the infrastructure risks of how that agent was deployed.

Inside the Initiatives: How Sages Are Building AI Agent Programs

Four active initiatives show AI agents deployed across security operations, incident response, customer service, and product development.

Replacing Tier-1 Analyst Capacity With an Autonomous SOC Agent

A Field CTO at an enterprise information technology and services organization is evaluating autonomous SOC platforms to own Tier-1 alert triage and investigation end-to-end. Integration depth with existing SIEM, EDR, and identity tooling is a hard requirement, as is a supervised launch mode with a credible path toward fuller autonomy as the platform earns trust. The evaluation language is direct: "It has to earn analyst trust fast, or it just becomes another console nobody opens."

Replacing Opsgenie With an AI-Driven Incident Response Layer

A mid-market insurtech VP is evaluating AI-driven incident response and paging platforms to replace Atlassian Opsgenie. The initiative targets the full incident workflow (AI-based alert validation, automated triage, and context aggregation from logs and metrics) and whether AI can own the investigation layer entirely, with the on-call workflow built around it rather than on top of it.

Evaluate AI Pager to Reduce Alert Noise and Improve Incident Response

Date Started Mar 15, 2026

Target Completion October, 2026

Speak with Sage

Tool Being Replaced

Atlassian Opsgenie

Use Cases

Alerting & Notifications

Evaluate AI-driven incident response and paging platforms to reduce alert noise, automate triage, and improve MTTR. The goal is to replace or augment PagerDuty/Opsgenie by integrating AI investigation with our monitoring, logging, and incident workflows.

Requirements include AI-based alert validation, automated triage, context aggregation from logs/metrics, Slack/Jira integrations, workflow automation, and ability to integrate with Datadog, Prometheus, and existing on-call systems.

Products Considering: Edra, Vibe AI

AI Agents for Voice and Digital Customer Service

A large financial services organization is evaluating AI-powered contact center platforms to handle voice and digital support channels, with real-time agent assist, automated call summarization, and AI-driven chat integrated into existing CRM and ticketing systems. The goal is to serve customers at scale without increasing headcount, and financial services compliance requirements shape the deployment model throughout.

Shipping a Net-New Agentic Product on Azure

An enterprise information technology and services organization is building a new product that uses AI agents to complete operations in virtualized Windows workloads running on Azure. Agents respond to triggers from a client application or web page and execute tasks inside a virtual desktop environment — a deployment model that requires both agentic infrastructure and the security tooling to operate it safely at scale.

Top AI Agent Vendors Sages Have Evaluated Recently

Three vendors have appeared most frequently across recent AI agent evaluations, each addressing a distinct layer of agentic infrastructure.

Dropzone AI deploys autonomous AI agents that investigate alerts, hunt attackers, and respond to threats without humans in the critical path. It integrates with 90+ security tools including SIEM, EDR, and cloud platforms, operating 24/7 with a full audit trail on every finding.

What Sages evaluated it for: Autonomous Tier-1 alert triage and investigation; SIEM, EDR, and identity tooling integration; auditable investigation trails; and data-residency controls so alert data stays in tenant.

Aembit provides identity and access management for AI agents and other workloads, giving every agent a verified identity and enforcing policy-based, short-lived access at runtime. Its MCP Identity Gateway lets agents access enterprise systems without ever handling credentials directly.

What Sages evaluated it for: Identity-aware access control and least-privilege enforcement for agents; visibility over MCP servers and IDE integrations; and supply chain risk management within cloud and DevOps environments.

Tenet Security is a runtime defense layer for AI agents that discovers and maps their entire decision trees in real time, detecting lateral movement, unauthorized data passing, and agentic logic traditional monitoring misses. Its Agent-Side Simulation™ engine sandboxes tool calls before execution.

What Sages evaluated it for: Detecting lateral movement and unauthorized data passing in agentic workflows; pre-execution validation of agent tool calls; monitoring for prompt injection and data leakage; and runtime defenses that adapt as models change.

Sages' Key Consideration Factors

When evaluating AI agent platforms, security and AI leaders are largely making first-purchase decisions in a category without established incumbents. The evaluation criteria reflect that.

• ‍Stack Integration as the Baseline Requirement: Security leaders are evaluating how well an agent fits into what already exists: SIEM, EDR, SOAR, identity platforms, CRM, and on-call tooling. Agents requiring architectural changes are disqualified early.‍
• Explainability and Auditability as Non-Negotiables: Agents that act without leaving a reasoning trail create more risk than they resolve. Every verdict and agent action needs to be auditable and defensible, since executives need to defend those conclusions to their teams, boards, and auditors.‍
• The Autonomy Dial: Supervised First, Scalable Later: No leader gives full autonomy to an AI agent on day one. The programs gaining traction start in supervised mode (agents recommend, humans approve) with a path toward greater autonomy as trust builds.

Bottom Line

AI agents are running in production across security operations, incident response, customer service, and product development. The leaders getting this right treat agents as infrastructure that extends human judgment, and they build accordingly from day one.

Want to see the specific requirements and evaluations for your peers' AI agent initiatives?