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Ensuring Responsible AI

Use Cases for Agentic Performance Management Software

In the last year we've learned that the moat for AI isn't at the model level- it's at the application layer.

Five things are driving this:

  1. The enterprise/problem context is more important than the LLM
  2. Working with existing noisy data is more important than working with "clean data"
  3. There is no path to scaling context in LLMs due to context window as well as compute and latency issues at scale.
  4. Focused small models working in specific local context and general enterprise context is where AI is performant and shippable. This is also the way humans work.
  5. LLMs have more knowledge awareness and computational prowess, humans more context awareness and abstraction prowess. THIS LIVES IN THE APPLICATION LAYER, NOT THE MODEL OR DATA LAYER.
Agentic Performance Dashboard
Real-time Anomaly Detection

Identifying and Addressing Performance Drift

Anomalies in agent behavior, such as unexpected outputs, increased latency, security breaches, and misaligned behavour are unavoidable. There is no model layer solution. However, by using performance management at the application layer we can mitigate performance issues in realtime, ensure a safe application that does no harm.

GRC- Governance, Risk and Compliance

Ensuring Regulatory Compliance

GRC is there to ensure that failures in some part of the system do no harm. The best GRC strategy is one built into your operating procedures and workflows that demonstrate compliance and the systems and controls to ensure compliance in the future.

Most consultancies are writing documents that check at box but do nothing to get Agentic solutions into operations.

Building a solution with embedded operational risk management and process safety management capabilities is the essential foundation for GRC. GRC without APM is a castle built on sand.

Bias Detection and Mitigation

Promoting Values Alignment and Inclusitivity

Whether you do this for compliance and legal reasons, revenue goals, or for a philosophical reasons, the solution is the same.

Humans are very good at context, and can shift to the right communication strategy easily.

Models are trained on large set of general Internet data and can reason from different narrative points or view, a consensus point of view, or a random point of view.