Do you really know what your agents are doing when you are not looking?OpenAI hacks HuggingFace

How Runsphere helps yousecure your agents

Your agents don’t just generate text. They call tools, read data, write to systems, execute commands, and take actions for users in real time.

“With great power comes great responsibility.”
Uncle Ben

The hard part is controlling what they may do in production, across every session, against attacks that keep changing, without breaking the product for everyone else.

Problem

Users can attack the system. They try to override instructions, inject prompts through messages and tool arguments, and run unsafe commands. Some attacks are a single request. Others take many steps and look normal until the last action.

User risks such as prompt injections, harmful requests, and multi-step attacks meet model risks such as harmful outputs, rogue actions, and unexpected behavior inside the agent.

Models are a source of risk too. Agents go off-task. They call tools they were never allowed to use. They leak data, issue refunds, send mail, or run destructive commands because the model decided that was next. A bad output is not a bad paragraph. It is an action.

You need a system that watches both, and stops the action before it runs.

Integration

Runsphere is a control layer in front of your agents. It does not depend on the model you use today, or the one you switch to next month.

Keep your SDKs. Point them at Runsphere with a package swap or a proxy URL. Every new event in a session is checked against policy before it proceeds.

Runsphere covers:

  • User supervision. Attempts to override instructions, manipulate the agent, or request work that is not allowed
  • Model and action supervision. What the model actually does: tool calls, data access, commands, and side effects
  • Abuse and anomaly detection. Patterns that look fine in one step and dangerous across a session
  • Policy on the full surface. Models, users, tools, data, and actions, scoped per agent, per workflow, or per environment
  • Decisions you can prove. Allow, ask for approval, or block, with a trace of why
“If you run thousands of agents, you need automated runtime monitoring to keep them safe.”

How this works

Most teams that try to secure agents in-house write a few prompt filters, add allowlists around tools, then find the next failure in production. Each new policy is another special case. It does not keep up.

With Runsphere you describe how agents should behave. What they may access. What they may do. What must never happen. Policies can be scoped per agent, per workflow, per event type, or per environment.

At runtime, Runsphere sits on the path:

  • Input guard. Inspect the request for injection, off-task intent, and tools that are not allowed
  • Insights. Score risk against how this agent and this user usually behave
  • Routing. Apply the right policy pack, including a human review path when the action is high impact
  • Action guard. Stop tool abuse, data leaks, destructive commands, and spend that exceeds limits
  • Validation. Check that the outcome still matches the approved schema and instructions

You can change models or providers without rebuilding the control layer, because Runsphere is independent of the model stack.

runsphere sits between the agent and the action. It enforces policy, supervises the model, scores risk, and detects abuse, then allows, reviews, or blocks the action.
“The right security solutions enable engineering teams to move fast instead of blocking them.”

Latency

Agent products are conversational. Every millisecond you add is felt. A safety layer that makes the product slower gets turned off, or bypassed.

Runsphere is built to run in the live path. Typical added latency is under 60ms. For normal users, the control layer should be invisible. They only notice it when an action should not have happened.

Tooling

Runsphere shows you what agents and users are doing:

  • Live traces of what the agent tried, and what was allowed or blocked
  • Alerts for odd patterns and new attack shapes
  • Audit logs for incident review and compliance
  • Policy testing before you roll a change to production
  • Approval workflows for actions that need a person
  • Investigation views that show which policy fired, which did not, and the evidence

Every agent action should be visible, governable, and stoppable. You should be able to explain the decision afterwards.

All systems operational

Works with

  • OpenAI
  • Anthropic
  • Google
  • Microsoft
  • Meta
  • Mistral
  • and self-hosted model deployments
© 2026 Runsphere

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