Welcome to this special report from Cloud Wars’ Agent & Copilot, where we analyze the opportunities, impact, and outcomes that are possible with AI.
In this episode, Brent Wodicka, CTO of AIS, discusses a new organizational construct that AIS is sharing called the Analyst-Agent Pair, which aims to get the most productivity out of human workers who tap into AI agents for scalability.
Highlights
Defining Analyst-Agent Pair (02:12)
The model aims to structure human-AI collaboration, making AI tools active teammates rather than just chatbots or research tools. The concept evolved from observing early adopters of AI tools and the need for a formal structure to embed AI into workflows.
Wodicka explains that the agent assists the analyst, who remains the domain expert responsible for the quality of work and decision-making. The agent can invoke other agents, use APIs, or access data directly to scale the analyst’s capacity. The agent acts as the analyst’s second brain, capable of learning from feedback, automating routine tasks, and taking action.
Finance Transformation with AI (06:42)
Wodicka provides a detailed example of how the Analyst-Agent Pair model works in financial planning and analysis, or FP&A. The agent is connected to internal and external data sources, automating data collection and forecasting. The analyst uses the agent’s output to explore scenarios, validate assumptions, and focus on strategic decision-making. The model aims to reduce the time spent on routine tasks and improve the quality of financial analysis.
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Role — and State — of Orchestrators (09:52)
Wodicka explains the role of orchestrators: they coordinate interactions between multiple agents, ensuring agents work together effectively. Orchestrators handle complex workflows that span multiple departments, preventing agents from colliding and ensuring data accuracy. The concept of orchestrators has evolved over time, now focusing on coordinating agent interactions rather than just data retrieval.
While progress is being made, orchestrators are still in their early stages. He mentions the Microsoft AI Foundry orchestrator as an advanced platform but notes that significant work is needed to make it reliable for complex scenarios.
Evaluations, Observability, and Guardrails (12:51)
Wodicka introduces three critical elements of the Analyst-Agent Pair model: evaluations, observability, and guardrails. Evaluations involve measuring the usefulness and accuracy of agents, enabling a feedback loop to improve performance. Observability provides visibility into the agent’s operations, aiding in diagnosing errors and building trust in the system. Guardrails define the constraints and rules for agents, ensuring compliance and security.
Metrics for Success (15:45)
Wodicka emphasizes the importance of adoption, effectiveness, and trust as key metrics. Adoption measures the frequency of use in workflows, indicating the system’s integration into daily processes. Effectiveness focuses on the quality of output and its impact on business outcomes. Trust is crucial for maintaining engagement and ensuring the system’s long-term success.
Progression and Goals for AI Adoption (19:08)
Wodicka suggests setting goals for the percentage of critical workflows automated and tracking progress over time. He also recommends measuring the percentage of actions or outcomes resulting from AI agents within individual workflows. Success can vary by organization, but setting clear goals and tracking progress is essential for achieving long-term success.
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