Realizing the full potential of agentic AI in the enterprise

1. Power grid load balancing 

• Scenario: Agents monitor real-time electricity demand, renewable energy inputs and grid stability across multiple states 
• Outcome: Improvement in grid efficiency, reduction in brownouts and savings through automated load distribution and predictive maintenance. 

2. Document reconciliation and processing 

• Scenario: The agent ingests data from multiple ERP systems, proactively identifying mismatches and can complete forms and correct errors. Humans only intervene in cases or errors not fixable by RAG (Retrieval Augmented Generation) review. 
• Outcome: Organizations typically see faster closure rates and fewer manual errors, though the agent must integrate with multiple data platforms. 

3. Customer support and ticket resolution 

• Scenario: Agents triage inbound queries, parse them against existing and improving knowledge bases and route complex cases to specialized human reps. Over time, they learn from resolved tickets to improve their handoff accuracy. 
• Outcome: Faster response times, better resolution rates, customer satisfaction metrics — provided a robust fallback exists for uncertain queries. 

4. Operational monitoring in the supply chain 

• Scenario: Agents monitor shipment data, predict potential disruptions (weather events, supplier delays) and notify managers proactively. 
• Outcome: Reduced downtime, more agile rescheduling, with humans in the loop for final decisions on re-routing or supplier switches. 

Key observations 

In all the above examples, the need for guardrails, supervision and human judgement is clear. The risk introduced by orchestration gaps can produce conflicting or erroneous results. Even a small error rate in a model can compound rapidly over multiple steps with multiple agents in a complex orchestrated process, as Demis Hassibis of Google Deep recently reiterated. The need for humans in the loop is essential, but without understanding the cognitive load, we put humans under conditions that are prone to make the human-AI hybrid error-prone. Finally, cultural acceptance is key to any automation and Agentic AI is no different. Without employee buy-in and addressing the fear of job loss, the risk of organizational rejection can be significant. 

Challenges and future directions 

While small proofs of concept look promising, truly enterprise-wide deployments demand robust infrastructure, standardized toolkits and extensive user training. It is important to distinguish the non-deterministic nature of agents that can take different paths versus traditional rule-based software. Correcting and improving agentic behavior requires many iterations with improved data. In addition, agent infrastructure needs to also incorporate the software practices of lifecycle management, versioning, built-in learning and clearly built governance and compliance rules (especially in applications for regulated industries. 

Current large language (and reasoning) models excel at pattern matching but can struggle with logic or domain constraints. A neuro-symbolic hybrid — where a symbolic reasoning module enforces rules or knowledge graphs — could improve agent reliability while still leveraging the adaptive strengths of neural models. LLM/LRM-based agentic systems will perform better with the evolution of true reasoning that is currently lacking.