Agentic AI
Agentic AI

Agentic AI: The next frontier in automation

Most Robotic Process Automation (RPA) and hyper-automation covers just 30% to 40% of tasks. Agentic AI takes it further with autonomous, adaptive and cognitive intelligence.


In brief

  • Agentic AI is emerging as the next frontier in automation, addressing limitations in Robotic Process Automation (RPA) and unstructured data processing by enabling cognitive capabilities and dynamic decision-making for complex tasks.
  • With its human-like architecture, Agentic AI processes unstructured data, applies reasoning, and uses memory to enhance task execution and reduce errors.
  • Diverse workflow automation—sequential, parallel, hierarchical and iterative—allows organizations to implement tailored automation and AI-driven solutions that adapt to evolving business needs.

 

Robotic Process Automation (RPA)

Over the past decade, Robotic Process Automation (RPA) has evolved from simple task-based tools into sophisticated hyper-automation platforms. These platforms integrate various AI services to meet diverse cognitive demands for end-to-end automation. However, real-world applications often fall short, with automation covering only 30% to 40% of processes, mainly targeting rule-based tasks. This limitation stems from several factors:

  • Lengthy and costly AI service implementation
  • Dependence on training data
  • Dynamic and evolving business process steps
  • Frequently changing application interfaces requiring ongoing maintenance
Automation Gap " The Reality"

Organizations initially expected high return on investment from RPA by automating high-volume transactions. However, as Automation Centres of Excellence (COEs) have addressed rule-based tasks, identifying new use cases that meet ROI expectations has become challenging. These use cases often demand cognitive capabilities, cost-effectiveness, predictability and trust. Many struggle to sustain ROI as evolving business needs outpace technological readiness—calling for improved strategies to boost automation ROI.

Agentic AI addresses these limitations by processing unstructured data and enabling dynamic decision-making. It can independently perform tasks, use tools, and respond to feedback. Unlike GenAI, which offers insights, Agentic AI applies reasoning to achieve goals and execute actions, delivering measurable outcomes. Through cognitive automation, it strengthens business process optimization and intelligent process automation.

With its evolving capabilities, Agentic AI benefits from innovation across the ecosystem, including start-ups, providing Automation COEs with new solutions to explore. 

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:

  • 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.
  • 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.
  • 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).
  • 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.
Language models

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:

  • 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).
  • 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).
  • 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).
  • 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.

Summary

As organizations transition from automation to emerging technologies, Agentic AI represents the new frontier, bridging gaps left by traditional automation and enriching the technology landscape. With its autonomous decision-making, dynamic decision making and robust architecture, Agentic AI has the potential to unlock efficiencies and deliver significant value to organizations.

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