SandboxAQ Switch AI Tracks AI agent cost and risk

As enterprises move from AI chatbots to autonomous agents, a new problem is emerging: nobody can clearly say what each AI agent costs, what risks it carries, or whether it is worth the money. On October 19, 2026, SandboxAQ announced an expansion of its Switch AI platform that tackles exactly that gap, introducing two new tools — Switch Trust and Switch ROI — designed to make enterprise AI agent cost, security, and performance measurable per agent, according to the company's announcement.

The launch frames agent management as an accountability problem rather than an infrastructure one. The first generation of enterprise AI was measured through seats, usage, and token consumption, SandboxAQ noted; agents, by contrast, make decisions and take actions alongside people, which creates new expectations for oversight. Just this week, rival vendors have attacked AI agent cost from the other side: MiniMax's M3.1 Flash release cut model costs for agent workloads by 70 percent, while SandboxAQ is now tackling the spend-management side of the same problem. Switch Trust and Switch ROI build on the company's open-source Switch Rooms offering, which brings people and agents together inside shared, company-controlled context across Slack, Microsoft Teams, and Discord.

Switch Trust: Finding, Testing, and Guarding Every Agent

Switch Trust is meant to make every agent safe to put on real work, according to SandboxAQ. It works in four stages. Discover inventories the agents, models, tools, MCP servers, and dependencies running across connected environments and code repositories — including agents the security team may not know exist — and records ownership, source, dependencies, and severity findings.

Scan analyzes agents in code and the cloud for security vulnerabilities, combining static analysis with AI-assisted triage to filter false positives, assigning severity scores and mapping supported issues to OWASP agentic security categories with recommended fixes. Evaluate connects directly to an agent and attacks it with adversarial and functional probes — prompt injection, jailbreak, data-leakage, and instruction-adherence tests — with each attack producing a pass-or-fail result that feeds an overall health score. Finally, Monitor protects agents in production through an SDK that captures what agents actually do, including prompts, responses, model calls, latency, and policy events, with guardrail policies that can block, redact, alert on, or log unsafe behavior while preserving evidence.

The adversarial testing angle reflects how quickly attack research is moving: as reported by Unite.AI, the tooling positions itself as a vendor-neutral way to assess agent risk across whichever agents, models, and providers an organization uses. A real-world deployment is already underway: Mount Sinai West's MSW Lab is working with Switch Trust to build G Board, a framework that lets physicians create clinical applications using AI agents, according to the announcement.

Switch ROI: Tying AI Agent Cost to Business Outcomes

Switch ROI focuses on financial discipline for AI agent cost control, according to the company. It tracks spend and performance at the individual agent, session, and turn level — with budgets, burn rates, and alerts so teams catch overspend early — and connects that spend to real outcomes such as tickets resolved, pull requests accelerated, or tasks automated, rather than infrastructure metrics like tokens or API calls. The tool calculates net return on investment against human baselines and recommends concrete optimizations to cut waste from model choice, unused tools, excessive context, and looping behavior, as tipRanks detailed in its coverage of the announcement.

SandboxAQ is betting that enterprises will buy an independent layer for agent security and cost control rather than relying only on the tools bundled by their existing cloud and AI vendors, RuntimeWire observed, and it is pricing the entry point aggressively: a free plan covers up to five managed agents. The bet has a plausible economic logic. SandboxAQ executives warned that unmanaged agents can quickly become more expensive than headcount, and market watchers are already skeptical of the current agentic wave — industry analysts at Gartner expect more than 40 percent of agentic AI projects to be canceled by 2027, with only a small minority of leaders reporting proven AI returns.

Why This Launch Matters Now

The announcement lands at a moment when the agent economy's unit economics are under scrutiny from every direction. Model vendors are racing to cut inference prices for agent workloads, banks are restricting what outside agents may do with customer money, and regulators are beginning to ask who is responsible when an autonomous system acts. A measurement layer that ties AI agent cost and spending to outcomes — and subjects agents to the same vetting an employer would apply to a new hire's qualifications, cost, and performance — answers a question many enterprise AI teams have been asking internally.

SandboxAQ launched Switch in August 2026 as an open-source offering under the Commons Clause Apache 2.0 license, built around the premise that companies should be able to choose and change AI agents, models, and providers without losing control of their context. By keeping context and intelligence under the enterprise's control while remaining vendor-neutral, the platform aims to reduce lock-in risk and let companies swap agents and models without losing accumulated knowledge. Whether enterprises adopt an independent agent-management layer, or let their cloud and model vendors supply the tooling, will be one of the more revealing competitive questions of the next year. For now, at least the measurement problem has a name — and a product attached to it.