In large and distributed enterprises, workflows are formalized to assure consistency and scalability as well as compliance. Yet, despite the best-designed SOPs, in most cases, the workflows fail in the execution step due to human dependency, lack of visibility and ad-hoc related exceptions. Agentic AI introduces a new perspective – one that extends beyond automation and rule-based engines towards developing a more goal-oriented system design. 

What is Agentic AI? 

Agentic AI refers to intelligent systems that have the ability to make decisions pertaining to a pre-specified goal state. Unlike other forms of automation that follow their instructions to the letter, agentic systems operate within the confines of a policy or formalized reward function. They analyse contextual information, forecast probable outcomes and choose the one action that is best suited to achieving the goal. 

Agentic AI doesn’t just wait for instructions but it can set its own goals, make plans, and take action to achieve those goals, just like a human might – Aruna Pattam (https://arunapattam.medium.com/) 

Traditional workflow systems (like BPM tools or RPA bots) constrain themselves within the fixed sequences and they fall short. The real-world operations are nonlinear in nature and there are: 

• Delays in approval permissions due to unavailability. 
• Changes in task dependencies as situations arise. 
• Exceptions that arise from overriding a policy (as yet not accounted in it) 

 These environments suffer from degraded compliance, increased manual follow-ups, and process latency. 

Agentic systems use elements of: 

• Reinforcement learning (RL) to optimize decision-making in dynamic environments. 
• Process mining to understand how workflows are actually executed (vs. how they’re documented). 
• Natural Language Understanding (NLU) to interpret requests or deviations logged in plain language. 

Multi-agent systems for coordination across workflows involving multiple stakeholders or departments. 

A use case of Agentic AI  

Let’s look at a multi-step procurement approval process: 

• The request is generated in the procurement system. 
• It needs to be approved to get departmental, financial or legal approvals. 
• Then POs are created and pushed onto the ERP. 

Agentic AI is an integrated system working across systems (such as by APIs or event listeners) that tracks real-time execution against the goal state (PO will be created within 72 hours). 

It works like this: 

• If legal approval is delayed, a nudge is sent to Slack or Teams. 
• If the finance approver is away, the system checks the policy for auto-delegation and, if allowed, reroutes the task. 
• If the delay crosses the SLA thresholds, an escalation to the supervisor is done with all context. 

No human intervention is required for any of these. The agent proceeds forward by constantly monitoring and intervening in line with its objective function. 

Driving compliance with feedback loops 

Agentic AI can learn from execution patterns:  

• They track and identify bottlenecks by analysing process logs;  
• Such delays occur if certain conditions are matched-delay patterns are identified by analysing vendor type, amount, department, etc. 
• They can adjust priorities within queues or workflow alternative design (e.g., do away with pointless checks on low-risk vendors). 
• The feedback loop makes an improvement on compliance over time—stringently enforcing the steps and developing the workflow based on behaviour. 

Enhancing Efficiency through Policy-Based Execution 

Suppose the enterprise introduces the following policy: 

“All on boarding requests should be processed in 5 working days, with all working days-day IT access, hardware allocation, and team orientation also completed.” 

The agentic system then converts the original policy into a goal state and reasons actively about each sub-process.  

• It uses event-based triggers to keep track of the closures of IT tickets,  
• Applies heuristics to predict when delays might be expected. 
• Uses a task orchestration engine to auto-create downstream tickets in response to the completion of upstream actions.  

Hence, process automation becomes adaptive, smart and on-purpose. 

Technical Benefits 

• Reduction in Latency: To reduce manual follow-ups and decision chain automation. 
• Policy Enforcement: By encoding policies as reward functions rather than scripts. 
• Process Observability: Through streaming logs and ML-generated insights. 
• Compliance Auditing: Traceability of who, what, when and why. 

Step by step procedure of implementation  

• Integrate the agentic layer – Connect to core systems (CRM, HRMS, ITSM, ERP) through APIs or event streams. 
• Define objective functions – What “goals define a successful outcome” look like; for example:
  • X-day on boarding 
  • Approvals under SLA 
  • Escalations down by 30%

• Set contextual constraints – Define edge cases and policy constraints for example auto-approve purchases under $1,000. 
• Deploy agents – Starting with assistive agents (suggestions and alerts) before proceeding with autonomous agents (actual task execution). 
• Monitor and Adjust – Dashboards to monitor all agent interventions; measuring process KPIs to tweak the reward functions. 

Agentic AI bridges the chasm between intention and realization. Instead of just documenting or converting workflows into instructions for execution, the Agentic AI drives the execution process, thus ensuring the workflow is religiously followed, adjusted or optimized in real-time. With that, organizations can lessen friction, improve compliance, and keep processes on track as business conditions change.