Kindo Raises $30 Million to Expand Enterprise AI Agent Control and Reduce Costs

Kindo has raised $30 million in a financing expansion to expand its enterprise AI agent management platform, bringing its total capital raised to $57.6 million. The financing was co-led by Crosspoint Capital and Forgepoint Capital, backing a company focused on a growing operational problem: keeping control of what AI agents can access, what they do, and how much they spend.

The Venice Beach, California-based company plans to use the money to expand its go-to-market organization and accelerate development of its AI Control Center and Deep Hat models and add-ons. Kindo reports approximately 800% year-over-year revenue growth in 2026.

The new round also brings board appointments. Andre Fuetsch, managing director at Crosspoint Capital, and Ernie Bio, managing director at Forgepoint Capital, will join Kindo’s board of directors.

A Control Center for Agents Built Across the Enterprise

Deploying an agent delegates authority. An agent connected to business systems may be able to read internal information, update records, approve requests, or modify infrastructure. As deployments multiply, security and operations teams need to understand both the permissions an agent holds and the actions it actually takes.

Kindo positions its platform as a shared operating layer for IT, security, finance, and compliance. Discovery maps sanctioned and shadow AI, while a central view brings together activity, access, costs, and outcomes. Organizations can build agents within Kindo or connect agents developed elsewhere. Kindo describes this as an AI Operations Center, bringing the continuous monitoring and intervention familiar from security and network operations centers to enterprise agents.

The company’s website describes a registry with agent ownership, permissions inherited from the person running an agent, and cost attribution. Those functions address different parts of the same management problem: identifying who is responsible, controlling authority, and determining whether the resulting work justifies its resource use.

Founder and CEO Ron Williams said the gap is not a shortage of agent builders. It is the tooling organizations need to control how agents are built, which data they can reach, where information can go, and what operating those agents costs.

Gateways Govern Calls; the Harness Adds Deeper Intervention

Kindo’s announcement describes gateways that govern and audit agent calls to connected data, tools, and approved models. Identity controls apply existing organizational permissions to people and agents, with the option to tighten access centrally.

This makes the connection between an agent and its dependencies a control point. A policy layer can govern access across supported integrations rather than leaving every agent team to implement its own disconnected controls. Its coverage depends on the agents and resources connected to that layer.

For mission-critical deployments requiring deeper control, Kindo offers a purpose-built agent harness. The harness is the environment that manages an agent’s execution and gives operators additional monitoring, support, and intervention capabilities, including taking over an agent when something goes wrong.

That distinction matters commercially and technically. Connecting an externally built agent through gateways is different from running it inside Kindo’s execution environment. The release specifically associates deeper intervention and several Deep Hat cost features with agents running in its harness.

Making AI Spending Visible Alongside Outcomes

Kindo’s AI Control Center records token consumption and agent-run outcomes, including failed runs, so spending can be attributed to individuals, agents, departments, and projects. Finance teams can set budgets, receive alerts, and enforce hard spending caps.

The value of that accounting is not limited to identifying the most expensive model. A low-priced agent that repeatedly fails can waste resources, while an expensive model may be justified for a difficult, valuable task. Connecting cost to outcome helps teams identify repeated failures, unnecessary model capacity, and work that could be handled more efficiently.

Kindo’s deployment options also give customers a choice of where the platform runs: Kindo’s cloud, a self-managed customer cloud, on-premises infrastructure, or an air-gapped environment. Deployment location and model selection are separate decisions, but both affect how an enterprise balances operational requirements, control, and cost.

Deep Hat Targets Different Sources of Model Cost

The Deep Hat family addresses several distinct ways AI workflows consume resources. The funding release describes four tools:

• Sift: Learns which events an agent would dismiss, then filters those events after a person approves the threshold. It records filtered events and sends a sample through for continued measurement.
• Tune: Adapts a smaller, lower-cost model to a particular task, leaving larger models for work that needs them.
• Shrink: Runs repeatable workflow steps as code to remove the model cost of those steps, routing exceptions back to a model when necessary.
• Attack: Supports private, authorized offensive security testing as an alternative to using frontier cybersecurity models for routine testing work.

Kindo’s published Sift explanation adds useful detail about the filtering mechanism. It starts in shadow mode, allowing every event to reach the target agent while learning from that agent’s verdicts. Human approval precedes activation, and a sample of events that would be filtered still reaches the agent to check for misses.

The design saves model work by deciding when a full agent run is necessary. Its measurement is against the target agent’s verdicts, however, so agreement with that agent should not be confused with independently verified ground truth.

Customer Adoption and the Next Stage of Expansion

Kindo says it runs in production at large and mid-market enterprises across financial services, professional services, healthcare, and aviation and transportation. The announcement highlights Aireon, whose CISO reported in May 2026 that using Kindo for threat hunting and Tier 1 and Tier 2 security operations helped generate more than $2 million in annual savings. That is a customer-reported outcome, rather than a guaranteed result for other deployments.

TFX Capital and CircleRock Capital participated in the financing alongside existing investors Drive Capital, Riot Ventures, RRE Ventures, Eniac Ventures, Marlinspike Partners, New Era Ventures, and Sunset Venture.

The $30 million financing funds a business built around the operational consequences of agent adoption. Creating agents is increasingly accessible; supporting them in production requires ownership, access policies, intervention mechanisms, and spending controls. Kindo’s opportunity is to make those responsibilities manageable across agents that were built in different places and serve different teams.