A Clear Path to Enterprise-grade Agentic AI

TQA’s Agentic AI methodology is a structured, proven way to transform enterprises.

By aligning trusted technologies, business value, and organizational readiness in a single framework, we provide a clear and comprehensive path to implementing AI agents. One that enables innovation and brings your people along on the journey.

What is Agentic AI?

Agentic AI systems consist of ‘autonomous workers’ that independently plan and execute tasks.

While traditional AI waits for prompts to handle specific tasks, agentic AI takes initiative and solves problems without step-by-step guidance. It can set goals, plan the steps, and execute actions, while involving humans for oversight and leadership when needed, or working entirely independently when not.

01

Goal-Oriented

Agents are built around a business goal you define. They break goals into tasks and work to achieve them.

02

Autonomous

Within defined guardrails, agents decide what to do next without waiting for new prompts. They can independently use your tools and systems to complete tasks.

03

Self Learning

AI agents can learn from feedback and results in your environment. When designed with secure learning mechanisms, they optimize how they plan and act over time, improving effectiveness within defined policies and guardrails.

04

Contextual

Agents understand business context. With a real-time view of your systems and interactions, they can adapt to each situation.

One giant leap for process automation

Agentic AI marks the shift from rule-based process automation to outcome-based systems that can plan, coordinate, and act with appropriate oversight.

Most organizations start by applying agents to a small number of high-value tasks to prove impact. Over time, those agents are extended to work across more workflows and connect through an orchestration layer that coordinates actions, manages handoffs, and keeps execution controlled and traceable, even when agents come from different platforms.

As capabilities grow, isolated improvements start to connect. You move from automating steps to improving whole workflows, with agents working together to reduce manual effort and improve speed and consistency across repeatable work.

Our Approach to Implementing Agentic AI in Enterprise Organizations

At TQA, we’ve implemented agentic AI in complex, highly regulated enterprises. Our Agentic AI Transformation Framework supports agentic leaders as they move from team readiness to enterprise-wide adoption, working alongside business and technology experts who understand what it takes to deliver safely in real operations.

While our approach is highly structured, it is adaptable and can be tailored to your unique enterprise needs.

1

Educate

We work with your leaders and teams, building a shared understanding of agentic AI and how it differs from traditional RPA and automation. We also establish what enterprise-grade implementation looks like in practice, and help shape your strategy for adoption.

2

Explore

We systematically analyze your processes to identify and prioritize practical, value-generating use cases across the business. We show how each use case delivers measurable impact. We demonstrate how each use case delivers measurable benefits.

3

Evaluate

We navigate and evaluate the complex vendor landscape, matching the right technology with your business requirements, existing investments, risk tolerance, and long-term scalability needs.

4

Enable

We establish the systems, processes, data pipelines, and organizational capabilities required to run agents with full peace of mind and control. We enable your business to continuously expand its agent ecosystem, reusing proven patterns to scale impact across functions and business units.

5

Embed

We go-live, transforming pilots into operational reality. With a focus on human–agent collaboration, we put governance into practice and ensure sufficient transparency for sustainable adoption.

6

Expand

We begin enterprise-wide adoption, implementing systematic scaling and repeatable delivery. This final phase lays the foundation for more workflows to be automated over time and for efficiency and performance gains to compound across the organization.

Frequently Asked Questions

Fast answers to the most common questions we hear about agentic AI and our implementation methodology. If you can’t find the information you’re looking for, please get in touch.

What is agentic AI, and how does it differ from traditional automation or RPA?

Agentic AI can act independently to plan and make decisions on how to achieve a goal.

Traditional automation and RPA run predefined steps in a narrow workflow. In contrast, agents can handle more open-ended tasks, work across systems, and respond to changing contexts.

How does TQA’s agentic AI methodology reduce the risk of “failed AI” projects?

TQA reduces risk by taking a structured path from readiness to scalable adoption, with governance and transparency built in. We help agentic leaders choose use cases that fit real operations, design an implementation approach that works with existing systems, and put monitoring and traceability in place so performance stays visible and controlled. This prevents stalled pilots and fragile deployments.

How do you measure the impact and ROI of agentic AI transformation?

We measure ROI by baselining a workflow, then comparing performance after implementation and translating the difference into financial and operational value. We track metrics like time to complete, cost to serve, error rates, and throughput, then tie improvements to a benefits case. The results guide which workflows to scale next and where to invest.

How does TQA’s methodology handle governance, compliance, and security?

We treat agents as first-class users with tightly controlled access and full traceability. Policies, approvals, and monitoring are defined up front, then mapped to your existing controls so every agent action is permissioned, logged, and auditable.

How does TQA maintain control as agentic AI programs move from pilots to scaled, enterprise-wide adoption?

We ensure control by putting clear guardrails in place before agents move beyond pilots. As more agents are rolled out, each must follow the same governance policies and standards. And as adoption grows, every agent operates inside the same visible, enforceable control framework.

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