IndyKite AgentControl secures how autonomous AI agents retrieve data, use tools, and take action. Runtime enforcement applies at every step, with full decision traceability across enterprise systems.
- Protect data as agents retrieve, use, and share it
- Enforce security controls on every agent action in real time
- Trace decisions across autonomous and multi-agent workflows
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Trusted by leading enterprises
Provable, not assumed
Apply live context directly to enterprise AI queries
Mobilize sensitive data safely across teams, partners, and AI systems
Prove compliance instantly, with full decision traceability
Build products and services that were impossible under static controls
Control agents in three steps
Connect your enterprise context
Apply dynamic, granular controls
Enforce and trace in real time
AI agent security questions
IndyKite AgentControl applies runtime control to every agent action. Using live context, agents are evaluated as they retrieve data, use tools, and interact with enterprise systems. Every decision is enforced in real time and fully traceable, providing the control required to deploy autonomous AI with confidence.
IndyKite AgentControl evaluates every agent action against a live knowledge graph of enterprise context. Signals such as provenance, data sensitivity, relationships, and current conditions help determine how data is retrieved, how tools are used, and what actions are appropriate. Every decision remains fully traceable for security and compliance.
IndyKite AgentControl evaluates each agent action against live enterprise context. As agents retrieve data, use tools, and interact with systems, security decisions are enforced in real time and recorded for investigation, compliance, and audit.
With AgentControl, every agent actionl is recorded and fully traceable. Organizations can understand what an agent did, what data was involved, and the conditions under which decisions were made, helping support security investigations, compliance requirements, and audit processes.
AgentControl evaluates every agent action against live enterprise context rather than relying on static permissions alone. Context such as data sensitivity, provenance, relationships, and current conditions helps ensure agents only perform actions appropriate for the task at hand.
AgentControl applies runtime control to every step in a multi-agent workflow. As agents retrieve data, invoke tools, or delegate tasks to other agents, each action is evaluated against live enterprise context and recorded for full traceability. This allows organizations to deploy multi-agent systems with confidence while maintaining security, governance, and accountability across the entire workflow.
Most organizations begin with a focused use case and a small number of agents. With AgentControl, IndyKite helps teams establish runtime control by connecting enterprise context, applying dynamic controls, and providing full traceability for agent actions. A focused demo can be tailored to your AI initiatives, systems, and governance requirements.
