Solving the Cold Start Problem for AI Agents

The infrastructure layer AI agents need to access enterprise resources safely and effectively.

The Cold Start Problem

AI agents are beginning to take on real tasks inside enterprises—triaging incidents, proposing remediations, generating reports, writing code. But the moment they try to interact with infrastructure, they stall.

  • They don't know what systems exist.
  • They don't have credentials.
  • They can't escalate access.
  • They don't understand policy.
  • They can't explain their actions.

This is the Cold Start Problem.

The problem isn't just technical—it's social. Most access flows in enterprises rely on human intuition, shared norms, or social trust. Agents don't have any of that.

Most enterprise systems weren't designed for workloads that originate dynamically, evolve in real time, or cross organizational boundaries. But that's exactly what agents do. They emerge, take action, and dissolve—often in seconds. And the current access model just can't accommodate that.

Empathic provides that infrastructure.

We provide dynamic, policy-driven access control and observability for AI agents operating inside the enterprise.

Our first product is a protocol-native database proxy that lets an agent securely discover and escalate privileges at runtime based on the task it's been given.

Enterprise Reality

The Current Access Model Breaks Down

In a typical enterprise, access control is layered and slow. Credentials are manually provisioned. Roles are hardcoded. Scopes are static. Even for a human employee, it can take days to get access to a production system.

You can't file a JIRA ticket for every ephemeral agent instantiation. What ends up happening instead is that teams overprivilege service accounts. They create long-lived credentials with broad access because it's easier than going through formal IAM processes.

The Observability Gap

Most access control systems can tell you who had access to what system, but not what they did with it. They don't track queries. They don't log result sets. They can't correlate access with task context.

Even if you're capturing access logs, they're too high-level to be meaningful. You might know that an agent hit a database, but you don't know what table it touched, what data it saw, or why.

Our Framework

We've built a comprehensive framework to address the Cold Start Problem for AI agents. Our approach focuses on four key principles that ensure secure, dynamic, and observable access to enterprise resources.

Runtime Access Provisioning

Access is provisioned at runtime. We do not rely on static roles, pre-scoped OAuth tokens, or long-lived credentials. Instead, access is granted dynamically when the task is issued, based on the requesting identity, the required data, and the policy in effect at that moment.

Direct Policy Enforcement

Policy is enforced directly in the data path. We don't delegate enforcement to upstream orchestration layers or rely on trust in the agent runtime. Every query is evaluated at the point of execution, inside the system that is being accessed.

Minimal Access Scoping

All access is scoped to the minimum necessary. Each session is ephemeral and bounded by identity, task, and system. There is no over-privileging. There are no generic role accounts with broad access.

Complete Observability

Everything is observable. We log and trace every query, every credential issued, every result returned, and every access path used. This provides the visibility required to validate policy enforcement, detect anomalous behavior, and build trust.

Our Vision

Once you control the connection and can observe the lifecycle of an agent, you can start creating a richer environment for that agent to operate—provisioning not just access, but relevant context, guardrails, and observability hooks. We don't just want to unblock access—we want to unlock trust.

Enterprises shouldn't have to redesign their infrastructure to make agents useful. The systems are already in place—identity providers, access policies, audit expectations. What's missing is the interface: a layer that understands how agents work, enforces policy in real time, and leaves no gaps in coverage.

Empathic makes agent access to enterprise systems safe by default, observable end-to-end, and governed in real time. No static roles. No long-lived credentials. No blind spots. This is how autonomous systems meet production standards—and how enterprises stay in control.