NeuBird AI sets trust model for production AI agents

NeuBird AI has published an open framework for governing autonomous agents' write access in production environments. It describes the model as a shared industry reference for operators, security leaders and engineers.

Called Earned Autonomy, the framework sets out a four-level trust model for moving agents from read-only activity to autonomous action. NeuBird AI is also inviting co-signers and revisions from companies and individuals involved in building, buying or running autonomous software that interacts with production systems.

The move comes as companies face growing questions over how much authority to give AI agents in live environments. Enterprise IT, site reliability engineering and DevOps teams are increasingly focused on whether autonomous systems should be allowed to make changes to production infrastructure, and under what constraints.

At the centre of the framework is a staged approach to trust. At Level 0, agents are limited to reading data and recommending actions. At Level 1, agents can prepare actions but require explicit human approval before making high-stakes or unfamiliar changes.

Level 2 allows low-risk, routine remediation to proceed automatically, but only within pre-cleared policy limits and defined blast-radius boundaries. Level 3, which NeuBird AI describes as earned autonomy, permits high-confidence operations to run autonomously inside strict virtual private cloud containment, with circuit breakers and automatic rollback mechanisms in place.

Promotion through the levels is based on verified performance, and an agent cannot raise its own access level. Write permissions remain bounded by policy and should be revoked if confidence falls.

Vinod Jayaraman, Co-founder and Chief Technology Officer, outlined the company's view.

"Enterprise buyers have moved past whether AI belongs in production. Now they're asking what the agent is allowed to do once it's been deployed," said Vinod Jayaraman, Co-founder and Chief Technology Officer at NeuBird AI.

He added: "Autonomy has been treated as a binary: either the agent is passive or it has root access. Neither is acceptable. Write access must be earned through demonstrated accuracy, bounded by policy and revocable the moment confidence drops."

Control model

NeuBird AI said the framework reflects the design of its own production operations agent and outlined four commitments that govern how its system operates in customer environments.

These include in-VPC execution, so telemetry and data remain inside the customer's environment; policy-bounded access using least-privilege and temporary credentials; human approval for higher-risk actions; automated rollback triggers; and an immutable audit trail recording rationale, context and actions.

Its production operations software can be deployed on premises, in private and public cloud environments, across hybrid systems, and in isolated or regulated installations, including air-gapped environments. The platform is SOC 2 Type II certified and designed with zero storage, with agent actions kept within predefined guardrails.

NeuBird AI is seeking broader adoption of the principles beyond its own products. The framework is open for use, amendment and co-signature by operators, security leaders and independent engineers, with the aim of creating a benchmark against which autonomous software vendors can be assessed.

That reflects a broader debate in enterprise technology over how AI agents should be supervised once they move from advisory roles into operational ones. While much of the market has focused on model performance and automation gains, permissioning in production has become a more prominent issue as organisations assess the risks of allowing software to make changes to live systems.

NeuBird AI argues that the industry has lacked a middle ground between tightly constrained observability tools and agents with broad system privileges. Its model attempts to define that middle ground by linking authority to evidence of performance while keeping controls in place for rollback, policy enforcement and oversight.

Jayaraman said the framework was shaped by demands from security teams for a more structured approach.

"Enterprise security teams have been asking for an architectural model that gives autonomous AI a defined lane in production, with boundaries that reflect real risk rather than a blanket restriction or an open door," he said.

He added: "The framework we've published is the model we run on ourselves. We're opening it because the industry needs a shared bar for what safe autonomous AI in production looks like."