What are AI Agents and Agentic Workflows?

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What are AI Agents and Agentic Workflows?

AI agents and agentic workflows represent a transformative approach to AI applications. These systems go beyond simple input-output mechanisms, creating autonomous entities capable of understanding, planning, and executing complex tasks with minimal human intervention.

AI Agents

AI agents are autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional AI applications that provide single-pass responses, AI agents can:

• Break down complex objectives into manageable tasks
• Execute actions independently
• Learn and adapt from experiences
• Interact with external tools and systems
• Make autonomous decisions based on context

Traditional systems follow predefined rules and fixed algorithms. Whereas AI agents operate with significantly more autonomy than traditional systems, making independent decisions and taking actions without constant human oversight.

Agentic Workflows

Agentic workflows refer to sequential and iterative processes where AI agents perform complex tasks through structured decision-making and execution. Key components include:

• Task Decomposition
  • Breaking complex tasks into subtasks
  • Mapping relationships between subtasks
  • Prioritising sequential execution
• Decision Making
  • Data collection and analysis
  • Processing information through cleaning and transformation
  • Selecting appropriate algorithms for optimal actions

Core Components in AI Agents

The foundation of AI agents relies on a sophisticated architecture that enables their autonomous operation and decision-making capabilities. Each component serves a specific purpose while working in harmony with others to create a cohesive system.

1. Profiling Module
   1. Determines agent function and role
   2. Defines operational scope
2. Memory Module: AI agents combine short-term memory, which helps process recent interactions, with long-term memory for storing and recalling prior context. This is often implemented with retrieval-augmented generation (RAG), enabling efficient access to stored data.
   1. Stores past behaviors and experiences
   2. Enables learning and adaptation
3. Planning Module: The AI agent develops and follows a step-by-step plan to accomplish a set goal. For example, an agent tasked with writing a report might outline topics, conduct research, and draft the report in sequence.
   1. Places agent in dynamic environment
   2. Enables strategic decision-making
4. Action Module: Agents can access external APIs and tools to perform specific tasks, such as retrieving information or executing user requests.
   1. Translates decisions into specific actions
   2. Executes planned tasks

Advanced Capabilities

Modern AI agents possess sophisticated abilities that extend far beyond basic task execution, enabling them to handle complex scenarios and adapt to changing circumstances with remarkable efficiency.

Tool Integration

AI agents excel at leveraging external tools and services, creating a powerful ecosystem of capabilities. This integration allows them to access diverse resources, from data analysis tools to specialised APIs, expanding their problem-solving capabilities significantly.

AI agents can:

• Access external API
• Execute code
• Perform web searches
• Use specialised functions
• Interact with databases

Self-Reflection

The ability to analyse their own performance sets AI agents apart from simpler systems.

Agents can:

• Review their own work
• Identify improvements
• Refine outputs iteratively
• Adjust strategies based on feedback

Implementation Design Patterns

The practical implementation of AI agents follows established patterns that ensure optimal performance and reliability while maintaining flexibility for various use cases.

Multi-Agent Systems

In agentic workflows, multiple agents collaborate, often dividing tasks or contributing unique perspectives to arrive at more comprehensive solutions than a single agent could achieve alone.

Multiple agents can collaborate, each with specialised roles:

• Task distribution
• Parallel processing
• Specialised expertise
• Collective decision-making

Planning and Execution

The systematic approach to task completion involves careful goal definition, strategic planning, and methodical execution.

1. Goal Definition
2. Task Decomposition
3. Resource Allocation
4. Action Execution
5. Result Evaluation

This structured process ensures reliable outcomes while maintaining adaptability to changing circumstances.

Practical Enterprise AI Agents Use Cases

AI agents find extensive use across various industries, demonstrating their versatility and effectiveness in real-world scenarios. Their ability to manage multiple tasks simultaneously while maintaining accuracy makes them particularly suitable for enterprise applications.

• Customer Service
  • Handling inquiries
  • Document processing
  • Issue resolution
  • Escalation management
• Financial Services
  • Portfolio optimisation
  • Risk assessment
  • Transaction processing
  • Compliance monitoring
• Software Development
  • Code generation
  • Bug fixing
  • Testing
  • Documentation