Control AI agents instead of running black boxes

The productive use of AI agents creates new requirements for transparency, quality, and cost-effectiveness. Multi-stage agent workflows access various models, tools, and corporate data and make autonomous decisions. Without observability, it remains unclear how results are generated, why quality fluctuates, or what costs individual agents incur. At the same time, companies must comply with regulatory requirements and document the responsible use of AI in a traceable manner. Only end-to-end transparency into agent behavior creates the foundation for secure and scalable AI operations.

Your benefits

  • Track agent executions and decisions end to end 
  • Transparently monitor costs, runtimes, and resource usage 
  • Continuously compare the quality and performance of agents and AI models 
  • Identify performance deviations early and optimize accordingly 
  • Maintain audit-ready documentation for AI governance and compliance 

Transparently track agent execution

Every agent run is fully documented – from the initial request, model selections, and tool calls to the final response. All intermediate steps remain traceable and can be analyzed in detail. This comprehensive observability facilitates error analysis, builds trust in AI-driven decisions, and lays the foundation for the controlled deployment of agents.

Transparently track agent execution

Every agent run is fully documented – from the initial request, model selections, and tool calls to the final response. All intermediate steps remain traceable and can be analyzed in detail. This comprehensive observability facilitates error analysis, builds trust in AI-driven decisions, and lays the foundation for the controlled deployment of agents.

Every agent run is fully documented – from the initial request, model selections, and tool calls to the final response. All intermediate steps remain traceable and can be analyzed in detail. This comprehensive observability facilitates error analysis, builds trust in AI-driven decisions, and lays the foundation for the controlled deployment of agents.

Monitor costs and resource usage

Runtime, token consumption, and costs are automatically tracked for each agent run. Companies can identify high-cost agents, compare models, and optimize their deployment based on objective metrics. This ensures that the operation of growing AI environments remains cost-effective, transparent, and predictable.

Monitor costs and resource usage

Runtime, token consumption, and costs are automatically tracked for each agent run. Companies can identify high-cost agents, compare models, and optimize their deployment based on objective metrics. This ensures that the operation of growing AI environments remains cost-effective, transparent, and predictable.

Runtime, token consumption, and costs are automatically tracked for each agent run. Companies can identify high-cost agents, compare models, and optimize their deployment based on objective metrics. This ensures that the operation of growing AI environments remains cost-effective, transparent, and predictable.

Continuously optimize models and agents

The quality and performance of different agents and AI models can be objectively compared using key metrics. Fourier AI Agent Operations continuously monitors quality metrics such as success rates, hallucination rates, and deviations from expected behavior. Response times, costs, and resource usage are transparently compared. This allows you to identify performance declines in individual models or agents early on and take targeted countermeasures.

Continuously optimize models and agents

The quality and performance of different agents and AI models can be objectively compared using key metrics. Fourier AI Agent Operations continuously monitors quality metrics such as success rates, hallucination rates, and deviations from expected behavior. Response times, costs, and resource usage are transparently compared. This allows you to identify performance declines in individual models or agents early on and take targeted countermeasures.

The quality and performance of different agents and AI models can be objectively compared using key metrics. Fourier AI Agent Operations continuously monitors quality metrics such as success rates, hallucination rates, and deviations from expected behavior. Response times, costs, and resource usage are transparently compared. This allows you to identify performance declines in individual models or agents early on and take targeted countermeasures.

Implement AI governance in a traceable manner

All relevant information on agent executions, model selection, and tool usage is documented and available for audit purposes. This enables companies to document the use of their AI agents in a traceable way and meet compliance requirements.

Implement AI governance in a traceable manner

All relevant information on agent executions, model selection, and tool usage is documented and available for audit purposes. This enables companies to document the use of their AI agents in a traceable way and meet compliance requirements.

All relevant information on agent executions, model selection, and tool usage is documented and available for audit purposes. This enables companies to document the use of their AI agents in a traceable way and meet compliance requirements.

Further information

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