Agentic AI: The next frontier in automation

Agentic AI architecture

The architecture of Agentic AI mimics human behavior. When given a task, humans start with a goal, apply experience, analyze context and adjust based on available data.

Agentic AI follows a similar pattern, enabling human-like execution. With the right architecture, organizations can achieve significant automation ROI through streamlined workflows and  comprehensive enterprise automation strategies.

At its core, Agentic AI leverages language models with cognitive capabilities to process unstructured data, reason and coordinate tasks. Understanding its components is key to building effective automation solutions. These include:

1. Planning and reasoning: When set up with instructions, language models use frameworks like 'chain of thoughts' or 'tree of thoughts' to create optimal plans based on context and input data. They apply reasoning through reflection loops, iterating actions until goals are met. In multi-agent scenarios, different language models can be assigned based on specific capabilities.

2. Tools: Agents interact with external systems using tools to execute planned activities. They utilize APIs or can be trained for GUI-based tasks, marking a significant advancement over Generative AI, which only provided insights.

3. Memory: Memory is vital for incorporating best practices and contextual knowledge. It allows agents to remember attempts and outcomes, enhancing execution. Agents typically have both short-term memory for specific transactions and long-term memory for enterprise data, enabling Retrieval Augmented Generation (RAG).

4. Guardrails: While agents' autonomy offers opportunities, it also risks incorrect execution. To mitigate errors from language models, guardrails must be established to define actions and when human approval is necessary.

Construct of agentic systems

Robust solution development is vital for Agentic AI. The solution designer leads by selecting agents, building workflows, and integrating tools. Much like a team lead assigns tasks and ensures access, designers configure agents with goals, instructions, guardrails and access to tools and memory. Each agent is built with capabilities that enhance reasoning and reduce hallucinations as follows:

1. Sequential workflow agents: Tasks are executed in a predetermined sequence, where the output of one activity serves as input for the next. Each agent is configured for a specialized activity and communicates only with the next agent. This method is used in well-defined processes requiring strict adherence to sequence (e.g., invoice processing: extract data, validate data, approval workflow, payment processing).

2. Parallel workflow: Tasks are executed simultaneously for greater efficiency and faster processing. Each agent operates independently, allowing concurrent work on different tasks. This approach is suitable for high-throughput situations where tasks can be processed independently (e.g., customer support: Agent A categorizes requests, Agent B reviews history, Agent C checks for live support).

3. Hierarchical workflow agent: Processes are categorized into groups of supervisory and subordinate tasks. This structure enables agents to manage complex processes with varying autonomy and decision-making capabilities. Each agent may oversee specific sub-tasks and have authority over lower-level agents, ideal for environments needing clear task assignment and supervision (e.g., document management: Agent A leads, supported by creation, review, and approval agents, along with archiving and compliance review agents).

4. Iterative workflow agent: Processing activities are executed in multiple iterations for continuous improvement. Agents can revisit previous steps based on feedback or results. This is ideal for scenarios needing ongoing adjustments based on performance metrics (e.g., app builder agents: Iteration 1 builds code; Testing Agent provides feedback. Iteration 2 redefines code based on feedback, repeating until the goal is achieved).

The choice of workflow depends on the nature and complexity of the business process.