Agentic Artificial Intelligence by Pascal Bornet

Author:Pascal Bornet
Language: eng
Format: epub
Publisher: Pascal Bornet
Published: 2025-10-15T00:00:00+00:00

Search Agent

Finds relevant articles on the web

Summarization Agent

Summarizes key points from the articles

Email Agent

Sends summaries via email every day

​Compiler Agent

Organizes content based on selected articles

Newsletter Formatting Agent

Prepares final newsletter

Manager Agent

Coordinates + final delivery

Table 8.4: Our team of specialized AI agents for the Newsletter automation (Source: © Bornet et al.)

This modular approach allowed each agent to excel in its specialized role while maintaining system flexibility through coordinated interaction.

Key Principles That Guided Our Design:

We followed six core principles in designing our multi-agent system:

1. Clarity of Purpose: “Each agent needs to be like a skilled specialist,” Rakesh explains. “Just as you wouldn’t ask a heart surgeon to also be an anesthesiologist, we gave each agent a clear, focused role.”

2. Efficiency Through Specialization: Rather than building complex agents that could do many things adequately, we created simple agents that could do one thing exceptionally well.

3. Scalability By Design: “We designed the system of agents to handle increasing workloads,” Rakesh shares. For example, if content volume increases, we can introduce multiple Search Agents working in parallel or optimize the Summarization Agent for faster processing.

​4. Autonomous Operation: Each agent operates independently within its domain, making decisions based on clear criteria without needing constant oversight.

5. Seamless Collaboration: The agents work together like a well-coordinated team, with standardized communication protocols and clear handoff points.

6. Centralized Orchestration: Our Manager Agent ensures all components work in harmony, much like a conductor leading an orchestra.

Design Principle #5: Build in Human-AI Collaboration

Perhaps the most crucial aspect of agent design is determining how humans and AI will work together. “The goal isn’t to remove humans from the process,” Nandan emphasizes, “but to create a symbiotic relationship where both humans and AI contribute their unique strengths.”

This principle becomes particularly important when we consider the current limitations of AI agents. At Levels 1-3 of our Progression Framework, agents still lack true adaptive learning and sophisticated reasoning capabilities. This means human expertise remains essential for:

1. Strategic Decision-Making: Humans excel at understanding context and making nuanced judgments

2. Quality Assurance: Providing oversight and catching subtle errors

3. Exception Handling: Managing unexpected situations that fall outside normal parameters

4. Continuous Improvement: Identifying opportunities for system enhancement

​When we first designed our newsletter system, we faced a fundamental question: Which tasks should be automated, and which should remain in human hands? Our approach was to think not in terms of what “could” be automated but what “should” be automated.

We identified two critical points where human input was essential:

The first key human touchpoint comes after our AI agents have gathered and summarized content. A human editor reviews the daily summaries and selects the most relevant pieces for the newsletter. This decision requires understanding our audience’s needs, recognizing emerging trends, and making nuanced judgments about content value—capabilities that remain uniquely human.

During implementation, we discovered an interesting pattern: when we allowed the AI to make final content selections, the newsletters became technically accurate but somehow soulless. They lacked the strategic narrative and thematic coherence that comes from human curation. By keeping humans in this crucial decision-making role, we maintained the newsletter’s distinctive voice and strategic relevance.

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