Instead of one monolithic agent, build a multi-agent system. Start with a simple classifier agent to determine user intent (e.g., sales vs. support). Then, route the request to a different, specialized agent trained for that specific task. This architecture improves accuracy, efficiency, and simplifies development.
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To discover high-value AI use cases, reframe the problem. Instead of thinking about features, ask, "If my user had a human assistant for this workflow, what tasks would they delegate?" This simple question uncovers powerful opportunities where agents can perform valuable jobs, shifting focus from technology to user value.
True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.
Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.
While consolidating tools seems efficient, using specialized, best-in-class AI agents for each GTM function (one for outbound, one for inbound) yields superior results. The depth and focus of specialized tools enable more powerful and nuanced use cases, justifying the management overhead of multiple systems.
Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.
A primary AI agent interacts with the customer. A secondary agent should then analyze the conversation transcripts to find patterns and uncover the true intent behind customer questions. This feedback loop provides deep insights that can be used to refine sales scripts, marketing messages, and the primary agent's programming.
Don't fear deploying a specialized, multi-agent customer experience. Even if a customer interacts with several different AI agents, it's superior to being bounced between human agents who lose context. Each AI agent can retain the full conversation history, providing a more coherent and efficient experience.
Separating AI agents into distinct roles (e.g., a technical expert and a customer-facing communicator) mirrors real-world team specializations. This allows for tailored configurations, like different 'temperature' settings for creativity versus accuracy, improving overall performance and preventing role confusion.
Instead of relying on a single, all-purpose coding agent, the most effective workflow involves using different agents for their specific strengths. For example, using the 'Friday' agent for UI tasks, 'Charlie' for code reviews, and 'Claude Code' for research and backend logic.
When developing AI capabilities, focus on creating agents that each perform one task exceptionally well, like call analysis or objection identification. These specialized agents can then be connected in a platform like Microsoft's Copilot Studio to create powerful, automated workflows.
