Agentic AI: The Rise of Autonomous Agents development in AWS, GCP, Microsoft, ServiceNow and Automation Anywhere

Agentic AI, a subset of artificial intelligence, is rapidly gaining prominence among business and solution architects as a transformative approach. Unlike traditional AI systems that primarily rely on predefined rules and data, agentic AI systems exhibit agency, meaning they can act autonomously, make decisions, and achieve goals in dynamic and complex environments on specific processes.

Example: Imagine an Agentic AI system designed to manage your personal finances. Use it to

1. Set a goal: Increase my savings by 10% in the next six months.
2. Plan: Analyze your spending habits, identify areas to cut back, and suggest investment strategies.
3. Execute: Automatically transfer money to your savings account, pay bills, and execute trades based on your investment preferences.
4. Adapt: Learn from market trends and your financial behavior to refine its strategies over time.

In this example, the Agentic AI system might use a smaller LLM like GPT-4o to understand your financial goals, generate personalized advice, and interact with your banks API.

How major platforms have adopted Agentic AI

Major platforms are incorporating AI to become more agentic.

1. Automation Anywhere uses AI-powered bots for intelligent automation, handling complex processes in AI Agent Studio.
2. ServiceNow 's Now Assist provides AI capabilities for workflows and virtual agents.
3. AWS offers services like Bedrock and SageMaker for building agentic AI applications.
4. GCP's Vertex AI and Generative AI Studio provide similar tools which uses Gemini.
5. Microsoft Copilot enhances productivity with AI assistance, with future potential for more autonomous workflows.
6. Salesforce Einstein Bots automate tasks and provide customer service.

These platforms are incrementally adding agent-like capabilities, focusing on practical applications and developer customization for specific needs. "Agentic" is a spectrum, and these tools are evolving towards more autonomous behavior.

Agentic AI: Overcoming the Limitations of Traditional AI

Traditional AI often struggles with complex, nuanced tasks, particularly in domains with:

• Ambiguous Language: Prompt-based AI systems can falter when faced with vague or ambiguous questions. For instance, a query like "What are the sales trends?" may be interpreted differently depending on the context and the specific information sought. Agentic AI, with its ability to learn from context and engage in dynamic interactions, can better understand and clarify such ambiguous requests.
• Domain-Specific Jargon: Industries like finance, healthcare, and hospitality have unique terminologies and slang. Traditional AI models may struggle to understand these nuances, leading to inaccurate or irrelevant responses. Agentic AI can be trained on domain-specific data and incorporate contextual knowledge to better comprehend and respond to industry-specific language.
• Complex, Multi-Step Processes: Tasks like month-end closing or hotel operations involve intricate workflows with multiple steps and dependencies. Traditional AI often struggles to handle these complex processes effectively. Agentic AI, with its ability to plan, reason, and adapt, can better navigate these intricate scenarios and automate tasks more efficiently.

How Agentic AI Solves These Limitations:

• Contextual Understanding: Agentic AI systems can learn and adapt to the specific context of a given task or domain. This allows them to better understand the nuances of language, including domain-specific jargon and ambiguous phrases.
• Proactive Learning: Agentic AI systems can continuously learn and improve their performance based on past interactions and feedback. This enables them to adapt to changing conditions and refine their understanding of complex domains.
• Goal-Oriented Behavior: Agentic AI systems are designed to achieve specific goals, rather than simply reacting to inputs. This allows them to focus their efforts on relevant tasks and make more informed decisions within the context of a larger objective.
• Dynamic Interactions: Agentic AI systems can engage in dynamic interactions with users and the environment, clarifying ambiguities, gathering additional information, and adapting their behavior accordingly.

By addressing these limitations, agentic AI is poised to revolutionize various industries, from customer service and healthcare to finance and manufacturing. As agentic AI systems become more sophisticated, we can expect to see even greater advancements in automation, decision-making, and problem-solving across a wide range of applications.

Challenges of AI development

Developing and deploying AI, especially agentic AI, presents significant challenges.

• Ethical concerns top the list, including bias in training data leading to discriminatory outcomes, the need for fairness in AI-driven decisions, and the lack of transparency in black box AI, hindering accountability. Job displacement due to automation is another key ethical consideration.
• Security and privacy are paramount. AI systems, reliant on vast datasets, become prime targets for data breaches, jeopardizing sensitive information. Data privacy must be protected, and systems must be resilient against adversarial attacks designed to manipulate AI behavior. The potential weaponization of AI raises further security and ethical alarms.
• Explainability is crucial. Understanding why an AI makes a specific decision builds trust and allows for the identification of errors or biases. Developing explainable AI (XAI) is a key research area, balancing accuracy with transparency.
• Finally, transparency and trust are essential for AI adoption. Openness about AIs workings, data usage, and limitations fosters public confidence. Collaboration between stakeholders, robust regulation, and public education are vital for navigating the complex ethical and societal implications of AI, ensuring its responsible development and deployment for the benefit of all.