As generative AI adoption continues to grow, leading organizations are beginning to leverage the technology as agents within larger applications. This use of agentic AI systems enables automation of complex business workflows, delivering impressive results and building on the already transformative potential of generative AI.
Using agentic AI systems implies more autonomy for the models. Because of this, LLM risks increase: prompt injection, misinformation, data leakage, and others identified in security frameworks like OWASP. To take full advantage of agentic AI while minimizing these risks, organizations must adopt aggressive approaches to monitoring and security.
How Agentic AI Workflows Transform Enterprise Operations
Agentic workflows allow organizations to unlock highly productive, domain-specific applications with transformative potential. Here are three areas where agentic AI systems create major impact:
Workplace Productivity
AI agents working together automate complex yet repetitive tasks, enhancing employee and organizational efficiency.
The results are measurable. Media organizations use AI agents to streamline content production, helping producers and editors scan and gather insights from thousands of hours of footage. The impact: 67% reduction in new hire training time, and information retrieval dropping from 24 hours to under 10 minutes.
At major technology companies, developers use agentic AI systems for code migration. One organization reported savings of approximately 4,500 developer years worth of effort, translating to $260 million in annual savings.
Business Workflow Transformation
AI agents automate niche, industry-specific workflows that previously consumed hours or days of human labor.
Financial institutions leverage agentic AI systems to automate mortgage compliance workflows, reducing errors by over 50%. Credit agencies use agentic AI for rapid risk report generation, cutting processing time from seven days to one hour.
Research and Innovation
Agentic systems automate complex research projects in specialized industries such as pharmaceuticals.
Pharmaceutical companies implement agentic AI solutions to enhance drug research processes. These systems automate approximately five years of research across various therapeutic areas, accelerating drug target identification and improving overall efficiency.
Challenges in Deploying Agentic AI
While these applications hold immense promise, as these systems scale, AI observability and security concerns become more critical. Common challenges include:
Data Quality: When data used to train or guide a model is poor-quality or inauthentic to real-world scenarios, model accuracy becomes compromised.
External Attacks: Bad actors attempting to reverse-engineer proprietary, public-facing models using model outputs.
Jailbreak Attempts: AI agents are potentially vulnerable against adversarial inputs that could trigger unintended behaviors or leak sensitive information.
To address these challenges, organizations must employ comprehensive AI agent security practices that include access controls, AI guardrails, adversarial testing, and AI monitoring. Protecting agentic systems requires a comprehensive strategy at both the model and application levels.
Model-Level Protection
At the model level, organizations should:
- Restrict access to model weights and inference endpoints
- Simulate adversarial scenarios in test environments before production
- Track model accuracy and safety over time using LLM observability
- Detect hallucinations, toxicity, and external attacks proactively
These practices help organizations identify and address risks before they cause reputational or monetary damage.
Application-Level Protection
At the application level, traditional security practices like authentication, authorization, and secure data transmission should be coupled with strict AI guardrails. Guardrails provide real-time detection and mitigation of hallucinations, prompt injection attacks, and other LLM risks. They autonomously intercept potentially dangerous prompts or responses before they reach the LLM or end user.
Monitoring Agentic AI Without Ground Truth
Monitoring AI agents is inherently complex, especially given the lack of ground truth in generative outputs. Traditional machine learning metrics like AUC or precision-recall are no longer relevant for capturing agent performance.
Enterprises should instead create composite metrics that evaluate end-to-end system performance through a combination of:
- LLM metrics: faithfulness, safety, coherence
- Business-specific performance indicators: task completion rate, user satisfaction, cost per interaction
- Quality signals: hallucination rate, policy violations, escalation frequency
Human-in-the-loop evaluation also remains critical, especially for high-stakes decisions. Mechanisms like pause or kill switches should be available when serious issues arise.
Scaling Agentic AI: Fundamentals
For organizations looking to invest in agentic AI, the following steps should inform implementation:
Build robust data infrastructure to power accurate AI. The quality of agentic outputs depends directly on the quality of data the system accesses.
Start with a single, high-value pilot use case to prove impact quickly while maintaining focus. Trying to do everything at once guarantees doing nothing well.
Establish MLOps foundations for monitoring, fine-tuning, and governance. These operational capabilities determine long-term success.
Train internal teams to follow uniform responsible AI practices. Consistency across teams prevents gaps that create risk.
Plan for scale by using enterprise-ready systems and tools from the beginning. Retrofitting is expensive.
Avoiding premature deployment, poor data quality, or stakeholder misalignment is key to proving value and avoiding costly risks.
Focus on What Won't Change
While the tools, models, and agents will evolve, the fundamentals of enterprise AI remain constant: delivering high-performing, accurate, and safe systems that enable strong ROI.
Whether it is a traditional ML model or a next-generation agentic system, the need for testing, monitoring, and governance will not disappear. Organizations that prioritize these constants will be better positioned to take advantage of agentic AI, staying ahead of competition and turning innovation into long-term advantage.
The capabilities are expanding. The risks are expanding too. The organizations that succeed will be those that build supervision infrastructure that scales with their ambitions.
