Agent Driven Dashboards - Business Story aligned to User Questions

• Agents will give Dashboards a voice as they will for data
• Data is static in dashboards today and with Agents suddenly will tell stories

Reactive and Proactive work with Agents

Keep Exploring!!!

Bridging the Skills Gap: Rethinking Education and Workforce Strategies in the Age of AI Agents

 $20 Code Agent Capabilities vs. Fresher Skills:

A $20 code agent will be provided, which individuals will need to run, test, and deploy. However, the skill gap between a fresher and the capabilities of this agent will be significant. This raises the need for a strategy to bridge this gap effectively.

Lack of Plan B in the Education System:

Our education system does not currently have a viable Plan B to adapt to such technological advancements. What additional measures can we take beyond utilizing agents to foster innovation? This is a critical area that requires rethinking and redesigning educational priorities.

Agents and Job Creation:

While agents are expected to enhance productivity, an important question remains: What new jobs will emerge as a result of this shift? Do policymakers and industry leaders have a clear vision or roadmap for these new opportunities? Ensuring that policies address this need for job creation is essential.

Keep Thinking!!!

Agents = All Business Logic in AI Tier

Satya Nadela explains the AI Agentic Future.

The business logic is all going to these Agents.

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Video from Bg2 Pod Youtube Channel (link in comment) pic.twitter.com/CCzcvPvmZ5

— Rohan Paul (@rohanpaul_ai) December 14, 2024

• Business Logic in Agents
• All Logic in AI Tier
• More AI Native Business Apps
• Data Analyst = AI Native Excel Apps (Visualization, Analysis)

Keep Creating Agents!!!

AI Agents

 Agentic World

• Customer support executive = Customer support agent
• Database administrator = Database Administration agent
• Software developer to Coding Agents

Two New Job Directions

• Agent creator jobs
• Agent Supervisors

Happy Agentic Solutions.

Ethical AI vs. Agentic Autonomous AI: Navigating the Complexities of Modern AI Systems

• Human Oversight vs. AI Independence: Ethical AI frameworks typically advocate for human-in-the-loop systems, ensuring human oversight. Agentic Autonomous AI aims to minimize human intervention, raising questions about responsibility and control.
• Short-term Gains vs. Long-term Consequences: The push for rapid AI advancement (often seen in Agentic Autonomous AI) may overlook long-term ethical implications. Ethical AI approaches tend to prioritize careful consideration of potential future impacts.
• The Reasoning Conundrum: While Large Language Models (LLMs) demonstrate language understanding and generation capabilities, they still lack true reasoning abilities. This limitation is crucial when considering the ethical implications of deploying AI systems in decision-making roles.
• Ethical Constraints vs. Autonomous Agency: The core tension between Ethical AI and Agentic Autonomous AI lies in balancing moral safeguards with the desire for increasingly independent AI systems. Ethical AI prioritizes human values and safety, while Agentic Autonomous AI pushes for greater AI self-direction.
• Transparency Trade-offs: Ethical AI often demands explainability and interpretability, potentially limiting model complexity. Conversely, highly autonomous AI systems may sacrifice transparency for increased capabilities, raising ethical concerns about accountability and trust.
• Data Ethics in AI Development: Ethical AI emphasizes the importance of unbiased, representative datasets. Agentic Autonomous AI, however, may prioritize data quantity over quality to enhance its learning capabilities, potentially perpetuating or amplifying societal biases.
• Continuous Learning and Ethical Drift: Agentic Autonomous AI systems that engage in continuous learning pose risks of ethical drift over time. Ethical AI frameworks must grapple with how to maintain moral constraints in evolving systems.
• Global Ethics vs. Local Autonomy: As AI systems become more autonomous, they may encounter scenarios where global ethical standards conflict with optimal local decisions. This tension between universal ethics and situational autonomy remains a critical challenge.
• Responsible AI Adoption in Practice: Implementing either Ethical AI or Agentic Autonomous AI requires a deep understanding of models, data, and their limitations. Superficial adoptions of either approach can lead to irresponsible and potentially harmful AI deployments.
• The Role of Human Values: Ethical AI explicitly encodes human values into AI systems, while Agentic Autonomous AI may develop its own set of values through learning. The alignment (or potential misalignment) of these values with human ethics is a crucial area of ongoing research and debate.

Technology will continue to change the world. A thoughtful approach is needed to prioritize use cases that offer broader positive impacts over those that primarily lead to monetization. This way of thinking can help align AI adoption with human values and ensure a more substantial positive impact on humanity.

Keep Going!!!

Good Paper Read - THE LANDSCAPE OF EMERGING AI AGENT ARCHITECTURES FOR REASONING, PLANNING, AND TOOL CALLING: A SURVEY

THE LANDSCAPE OF EMERGING AI AGENT ARCHITECTURES FOR REASONING, PLANNING, AND TOOL CALLING: A SURVEY

Key Summary notes

• AI agent architectures are either comprised of a single agent or multiple agents working together to solve a problem.
• Agent Persona. An agent persona describes the role or personality that the agent should take on, including any other instructions specific to that agent
• ReAct. In the ReAct (Reason + Act) method, an agent first writes a thought about the given task. It then performs an action based on that thought, and the output is observed
• Reflexion. Reflexion is a single-agent pattern that uses self-reflection through linguistic feedback

Dify is an open-source LLM app development platform

Beyond LLMs: Agents, Emergent Abilities

1. Agent is able to split / create smaller subtasks

2. Persona can be set for the agent, Few shot examples supplied to get the context

3. Multiple agents created for different purposes

4. Custom Agents / Agent to Agent communication etc..

Keep Exploring!!!