Agentic AI: A Practical Guide

In Part 1 of this series, we explored how to make generative AI systems robust and reliable for production. With that foundation in place, the next step is to give these systems the autonomy to act. This is where agentic AI comes in.

Until recently, autonomous, workflow-driven AI was limited by immature tools and infrastructure. That has changed. Advances in orchestration frameworks, models with structured function calling, and cloud-native architectures now make it possible to design agentic systems that run in real production environments at enterprise scale.

These advancements enable enterprises to automate complex workflows that were previously manual, slow, or error-prone, while maintaining control and transparency.

Agentic AI is already driving results in production:

• Hospitality – AI Concierge
  Managing thousands of daily guest requests end to end, coordinating reservations, transport, and housekeeping with minimal manual effort.
• Retail – Self-Correcting Product Catalog
  Continuously enriching and validating hundreds of thousands of SKUs, reducing manual workload by up to 80% while improving data quality.

The same principles apply in finance, healthcare, logistics, and other complex, integration-heavy industries.

Agentic systems bring intelligence directly into workflows. They connect applications, coordinate processes, and make real-time decisions with minimal oversight. At UFirst, we build these systems on three core pillars:

1. Reliable Orchestration
   Using frameworks like LangGraph, we design workflows that maintain context, recover automatically, and provide full traceability across systems.
2. Intelligent Execution
   We use language models either as standalone APIs or within enterprise AI platforms such as Vertex AI or AI Foundry, depending on security, governance, and scalability needs. For example, we recently applied Mistral, an EU-based model with strong function-calling capabilities, to meet specific data-sovereignty and compliance requirements.
3. Cloud-Native Architecture
   We build containerized tool environments with a clear separation of concerns between reasoning engines, integrations, and execution environments. This modular design ensures security, scalability, and the ability to evolve components independently.

Together, these pillars enable operational AI that executes real business processes, delivers measurable impact, and remains fully auditable and under enterprise control.

Before piloting agentic AI in your organization, consider three guiding questions:

• Workflows – Which business processes would benefit most from autonomy?
• Integration – Are your APIs and system connections ready to support automation?
• Governance – How will decisions be logged, audited, and monitored for compliance?

These questions help pinpoint where agentic AI can deliver the fastest and most tangible results.

Agentic AI represents the natural evolution of enterprise automation. It enables systems that actively contribute to business outcomes instead of remaining passive assistants. At UFirst, we help organizations make this transition by designing, deploying, and scaling agentic AI solutions that deliver tangible business impact.