Agentic AI consulting workflows for internal IT

Generative AI and Agentic AI Driving Workflow Automation and Productivity Gains

Estimated reading time: 10 – 12 minutes

Key Takeaways

• Generative AI is powerful for content creation and interpretation but remains reactive, while agentic AI is proactive, goal oriented, and capable of multi step action across systems.
• When combined, generative AI provides the brainpower for understanding and communication, and agentic AI provides the muscle to execute workflows end to end.
• Agentic AI is delivering the most value today in internal, structured workflows such as security operations, finance, logistics, and IT service management.
• Real productivity gains depend on redesigning workflows, selecting the right mix of rules, analytics, generative AI, and agents, and building shared platforms and observability.
• Eaton & Associates supports organizations with workflow assessment, secure AI platform implementation, and ongoing optimization to turn AI capabilities into measurable business outcomes.

Table of Contents

• Introduction
• What is the Difference Between Generative AI and Agentic AI
• How Generative and Agentic AI Work Together
• Where Agentic AI Excels: Workflow Automation Across Industries
• Rethinking Workflows to Unlock Real Business Value
• Choosing the Right Tool: Rules, Analytics, Gen AI, or Agents
• Measurable Productivity and Performance Gains
• Designing for Learning and Continuous Improvement
• Building Reusable AI Assets and Platforms
• The Future: Closing the Gap Between Intelligence and Action
• Practical Takeaways for Office Managers, IT Professionals, and Business Leaders
• How Eaton & Associates Can Help
• Ready to Explore Agentic and Generative AI for Your Workflows
• FAQ

Introduction

In the San Francisco Bay Area and beyond, organizations are racing to harness generative AI and agentic AI to automate workflows and unlock new levels of productivity. These two technologies are often mentioned in the same breath, but they play very different roles. When combined thoughtfully, they can transform how your teams work, how decisions are made, and how fast your business can move.

In this post, we break down what generative and agentic AI really are, how they work together, and how companies are using them today to streamline operations. We also share a practical roadmap for Office Managers, IT leaders, and business executives who want to safely apply these tools in their own environments, with examples of how Eaton & Associates Enterprise IT Solutions supports these transformations.

What is the Difference Between Generative AI and Agentic AI

Generative AI: Powerful, but Reactive

Generative AI is what most people think of when they hear “AI” today: models that can create new content on demand.

According to IBM, generative AI “creates original content in response to user prompts, including text, images, video, audio, and code.”

Thomson Reuters further explains that its primary strength is content creation and generation, operating reactively, waiting for input and then generating output based on that instruction.

Examples of generative AI in enterprise use include:

• Drafting emails, reports, and proposals
• Summarizing long documents or meeting transcripts
• Generating marketing copy or knowledge base articles
• Producing boilerplate legal or compliance language
• Writing or refactoring code snippets

Generative AI is an excellent co pilot for humans, but it does not act on its own. It does not log into systems, click buttons, or move data between tools. It responds; it does not initiate.

Agentic AI: Goal Oriented and Proactive

Agentic AI (or AI agents) represents the next leap: systems that do not just generate content, but take action toward a defined goal.

As Exabeam describes it, agentic AI is goal oriented and proactive. Instead of waiting for each individual prompt, an agent starts with a defined objective and works through multiple steps to accomplish that goal, continuously assessing progress and determining what needs to happen next.

Agentic AI systems are designed to:

• Perceive inputs from systems, data, or users
• Reason about what to do next
• Act across tools and applications
• Learn from results and feedback to get better over time

The key distinction is autonomy. Generative AI is powerful but mostly confined to “thinking and writing”. Agentic AI is built to operate with autonomy, making decisions and performing multi step actions with minimal human oversight.

In practice, that might look like:

• Monitoring a ticket queue, triaging issues, gathering logs, and drafting responses
• Orchestrating steps in a security incident response workflow
• Reconciling invoices across systems, flagging exceptions, and routing them for review

Generative AI helps generate content inside these workflows, but the agent runs the workflow.

How Generative and Agentic AI Work Together

These technologies are not competing; they are complementary.

Exabeam notes that agentic systems increasingly integrate large language models (LLMs), the engines behind generative AI, to operate flexibly in open ended environments.

Without generative models, agents would be stuck in rigid, rule based workflows. By embedding LLMs, agentic AI gains the ability to:

• Understand natural language requests
• Interpret messy or incomplete data
• Adapt workflows when something unexpected happens
• Communicate with humans in a conversational way

As LLMs improve with better reasoning, better summarization, and stronger contextual understanding, agents become more reliable at:

• Managing complex, multi step processes
• Adapting to changing inputs
• Collaborating with humans and other systems

In other words, generative AI provides the “brainpower” for understanding and communicating; agentic AI adds the “muscles” for taking action.

For IT consulting firms and enterprise IT teams, this combination enables a shift from passive assistance (“write this for me”) to active automation (“handle this entire process for my team”).

Where Agentic AI Excels: Workflow Automation Across Industries

Agentic AI shines when tasks extend beyond one screen, one app, or one click. It enables full workflow automation, coordinating sequences of actions that require judgment, coordination, and adjustment.

Here is how different sectors are using agentic AI today.

Manufacturing

Red Hat highlights manufacturing as a prime use case, where agentic workflows can:

• Manage supply chains end to end
• Optimize inventory levels
• Forecast demand
• Plan and adjust logistics in real time

Instead of static planning in spreadsheets, agents can continually ingest data from ERPs, IoT sensors, and vendors, and then act, placing orders, adjusting production schedules, or rerouting shipments.

Healthcare

In healthcare, Red Hat notes that agentic AI can:

• Monitor patient needs
• Carry out treatment plan workflows
• Provide personalized engagement and follow up

While clinicians retain final decision making authority, agents can automate routine communications, reminders, and documentation, helping care teams focus more on patients, not administrative overhead.

Software Development

Agentic AI can support the entire development lifecycle:

• Generating and debugging code
• Managing CI/CD pipelines
• Coordinating tests and deployments
• Designing or validating system architecture

Instead of developers manually hopping between tools, an agent can ensure tickets, branches, builds, and deployments stay in sync, while generative AI assists with code suggestions and documentation.

Financial Services

In financial services, Exabeam notes that agentic AI can:

• Continuously analyze market trends and economic signals
• Adjust strategies automatically based on new data
• Monitor credit risk
• Optimize investment portfolios in near real time

On the operational side, agents can also streamline compliance heavy workflows, extracting and validating complex financial information across multiple documents and systems.

Logistics & Supply Chain

Agentic AI can dynamically adjust logistics in flight. Exabeam highlights scenarios where agents:

• Reroute deliveries based on live traffic, weather, or capacity
• Reprioritize shipments by urgency or customer tier
• Reduce delays and improve utilization

This is especially powerful for Bay Area companies managing distributed inventories, multi carrier networks, or just in time operations.

Security Operations

Security is emerging as one of the strongest enterprise use cases. In security operations centers (SOCs), agentic AI can:

• Build automated timelines and case summaries
• Offload repetitive tasks like log parsing and report generation
• Guide analysts through structured investigation workflows

The result is less burnout and more focus on high value analysis and decision making.

Internal vs. Customer Facing: Where Are We Really Ready

A recent Harvard Business Review article argues that AI agents are not yet ready for most consumer facing work, but they excel at internal processes.

Why? Backend and operational processes:

• Are more structured and repeatable
• Involve clearer rules and data formats
• Have better guardrails and oversight

For Office Managers and IT leaders, this is good news: your internal workflows such as IT service management, HR onboarding, procurement, compliance, and accounting are exactly where agentic AI can safely deliver value today.

Rethinking Workflows to Unlock Real Business Value

One of the biggest misconceptions in AI consulting is the belief that “if we just deploy a powerful agent, value will follow”. Research suggests otherwise.

McKinsey analysis of early agentic AI adopters found that success requires fundamentally reimagining workflows, not just plugging AI into existing ones.

Organizations that focus only on “building the agent” often end up with impressive demos but underwhelming business results.

Start With Workflow Redesign, Not Just Technology

High performing organizations:

• Map out the end to end workflow
• Identify where people, processes, and technology interact
• Pinpoint user pain points and bottlenecks
• Design how agents and humans should collaborate at each step

This workflow first approach creates space to:

• Eliminate unnecessary work, not just automate it
• Decide where humans must stay in the loop
• Design clear handoffs and escalation paths

At Eaton & Associates, this step is one of the most valuable services an IT consulting partner can bring: a neutral, end to end view of your processes that internal teams rarely have time to assemble.

Choosing the Right Tool: Rules, Analytics, Gen AI, or Agents

Not every task requires an agent or even AI at all. McKinsey recommends evaluating tasks based on their characteristics and then choosing the appropriate technology.

Here is a practical decision guide you can use with your teams:

1. Rule Based & Repetitive Tasks
   Example: data entry, field mapping, routine file transfers
   Best fit: traditional rule based automation (RPA, scripts, low code workflows)
2. Unstructured Input (for example, long documents)
   Example: extracting key clauses from contracts; summarizing policy updates
   Best fit: generative AI, NLP, or predictive analytics
3. Classification or Forecasting from Past Data
   Example: predicting churn, classifying support tickets, forecasting demand
   Best fit: predictive analytics or generative AI assisted models
4. Outputs Requiring Synthesis or Judgment
   Example: drafting a board summary, combining multi source data into insights
   Best fit: generative AI
5. Multistep Decision Making with High Variability
   Example: end to end loan processing, complex incident response, multi system onboarding
   Best fit: AI agents (agentic AI)

In complex enterprise environments, the answer is often “all of the above”, a thoughtful mix of rule based systems, analytical AI, generative AI, and agents, all orchestrated together.

Orchestration Frameworks and Open Source Stacks

To manage this complexity, McKinsey recommends a common orchestration framework that lets agents:

• Call tools and APIs
• Combine outputs from different systems
• Share context and state across workflows

Popular open source frameworks include:

• AutoGen
• CrewAI
• LangGraph

These platforms allow agents to act as orchestrators and integrators, not just stand alone bots. For Bay Area organizations with multi cloud and hybrid infrastructures, this kind of integration is critical.

Measurable Productivity and Performance Gains

When agentic AI is deployed with the right workflows and guardrails, the productivity gains are substantial.

Security Operations: A Case Study in Impact

Exabeam reports that in security operations centers, organizations have achieved:

• Up to 80 percent faster investigation time thanks to automated timelines
• 50 percent faster incident response, with many manual steps removed
• 60 percent reduction in irrelevant alerts via risk based prioritization

These improvements do not just cut costs; they translate into better risk posture, faster containment, and reduced burnout in high pressure roles.

General Workflow Improvements

McKinsey highlights that when companies integrate validated services and reusable assets into a single platform, they can almost eliminate 30 to 50 percent of nonessential work in a given process.

In financial services, agents have successfully:

• Extracted complex financial information from multiple sources
• Automated information aggregation and verification
• Streamlined compliance analysis

This has reduced the amount of human validation needed while improving throughput.

For Office Managers and line of business leaders, similar patterns apply to:

• Accounts payable and receivable
• Contract lifecycle management
• Vendor onboarding and compliance
• HR onboarding and offboarding

Designing for Learning and Continuous Improvement

The most successful agentic AI deployments are not “set and forget”. They are learning systems.

McKinsey emphasizes that a critical success factor is designing agents to learn within workflows.

Every user interaction such as edits to AI generated content can be:

• Logged
• Categorized
• Fed back to engineers and data scientists

This feedback loop allows teams to:

• Refine prompts and decision logic
• Improve knowledge bases
• Teach agents new patterns and policies over time

As a result, the more frequently agents are used, the smarter and more aligned they become, creating a self reinforcing system of collaboration between humans and AI.

Monitoring, Evaluation, and Observability

With autonomy comes risk. That is why McKinsey recommends verifying agent performance at each step of the workflow and embedding monitoring and evaluation into the process.

One real world example: an alternative dispute resolution service provider noticed a sudden drop in accuracy. Observability tools pinpointed the issue: certain user segments were submitting lower quality data, which led to misinterpretations. With that insight, the team was able to correct course quickly.

For enterprise IT and automation projects, this means:

• Defining metrics for success such as speed, accuracy, compliance, and satisfaction
• Implementing logging and dashboards for agent decisions
• Setting up alerts when performance drifts or errors spike

This is an area where a seasoned partner offering IT consulting services can provide huge leverage, by designing and operating the monitoring stack so internal teams can focus on outcomes, not plumbing.

Building Reusable AI Assets and Platforms

Instead of reinventing the wheel for every workflow, leading organizations are building centralized libraries of validated AI assets.

McKinsey describes this as creating reusable:

• Services, such as LLM observability, authentication, or preapproved prompt templates
• Assets, including application patterns, shared code modules, and training materials

By standardizing and centralizing these components, you can:

• Accelerate deployment of new agents
• Enforce consistent security and compliance policies
• Reduce duplicated work across teams and business units

For Bay Area enterprises with multiple business lines, global offices, and hybrid IT environments, this platform approach is critical to scaling safely and cost effectively.

The Future: Closing the Gap Between Intelligence and Action

The convergence of generative AI and agentic AI is more than just a technology trend; it is a paradigm shift in enterprise automation.

Generative AI delivers productivity by augmenting human work such as writing, summarizing, and interpreting. Agentic AI adds autonomy, closing the gap between intelligence and action.

Industry estimates suggest autonomous agents could drive approximately 93 billion dollars in workflow value by 2030, as highlighted by McKinsey.

The direction of travel is clear:

• From individual task automation to end to end workflow orchestration
• From siloed tools to integrated AI platforms
• From manual oversight at every step to monitored, learning systems with targeted human in the loop controls

Organizations that begin experimenting and building capabilities now, especially in internal, structured workflows, will be best positioned to capitalize on these gains.

Practical Takeaways for Office Managers, IT Professionals, and Business Leaders

To translate these insights into action, use the following concise playbook in your organization.

For Office Managers & Operations Leaders

1. Identify 2 to 3 High Frustration Workflows
   Examples: vendor onboarding, contract approvals, employee onboarding, meeting scheduling and follow ups.
   Map the steps, systems, and handoffs.
2. Separate Work into Categories
   Simple, rule based tasks are candidates for RPA or low code automation.
   Document heavy or communication heavy steps are candidates for generative AI.
   Complex, multi step, cross system workflows are candidates for agentic AI pilots.
3. Start With Internal Facing Use Cases
   Prioritize workflows where errors are recoverable and can be audited easily.
   Involve your IT team or consulting partner early to address security and data access.

For IT Professionals & Enterprise Architects

1. Establish an AI Orchestration Layer
   Evaluate frameworks like AutoGen, CrewAI, and LangGraph for agent orchestration.
   Standardize how agents call APIs, use tools, and access data.
2. Build Shared, Reusable Services
   Centralize LLM access, logging, and prompt templates.
   Implement observability and monitoring from day one.
3. Design Human in the Loop Controls
   Define which decisions must be approved by humans.
   Implement approval workflows inside ticketing systems, collaboration tools, or ITSM platforms.

For Business and Technology Leaders

1. Align AI Initiatives With Measurable Business Outcomes
   Focus on reducing cycle times, error rates, or manual effort, not just “deploying AI”.
2. Invest in Change Management and Training
   Help teams understand how generative and agentic AI augment their work.
   Encourage feedback: every correction is valuable training data.
3. Partner Strategically
   Use external experts to accelerate discovery, design, and deployment, especially for cross functional workflows and security critical use cases.

How Eaton & Associates Can Help

As a San Francisco Bay Area based provider of Enterprise IT Solutions, AI consulting, and workflow automation, Eaton & Associates helps organizations:

• Assess which processes are ready for generative AI, agentic AI, or traditional automation
• Design end to end workflows that blend people, processes, and technology for maximum value
• Implement secure, compliant AI platforms with monitoring, observability, and governance
• Build reusable AI assets that can be applied across departments, from IT and security to finance and operations

Whether you are just beginning to explore generative AI tools or ready to pilot agentic AI driven workflows in your environment, our team can help you move from experimentation to measurable business impact safely and pragmatically through our comprehensive managed and consulting services.

Ready to Explore Agentic and Generative AI for Your Workflows

If you are based in the Bay Area or operate distributed teams that rely on robust enterprise IT:

• You likely already have workflows that could benefit from AI driven automation.
• The key is choosing the right mix of generative and agentic AI, with the right guardrails.

Eaton & Associates can help you:

• Identify high value automation opportunities
• Design and implement pilot projects for internal workflows
• Build a scalable, secure AI foundation for future growth

Contact us today to discuss your automation goals or to schedule a workflow assessment with our AI and IT consulting team. Let us help you turn generative and agentic AI from buzzwords into concrete productivity gains for your organization.

FAQ

What is the main difference between generative AI and agentic AI?

Generative AI focuses on creating content such as text, code, images, or summaries in response to prompts. It is reactive and does not act autonomously. Agentic AI is goal oriented and proactive, capable of perceiving inputs, reasoning, and taking multi step actions across systems with a defined objective, using generative models as a core capability.

Where should organizations start using agentic AI safely?

Following guidance from Harvard Business Review, the best starting point is internal, structured workflows such as IT service management, HR onboarding, finance operations, compliance tasks, and security operations. These processes have clearer rules and better oversight, which makes them ideal for early agents.

Do all automation projects require AI agents?

No. Many tasks are better handled by traditional rule based automation or analytics. As McKinsey notes, you should match the solution to the problem: use RPA for repetitive rules, generative AI for unstructured content and synthesis, predictive analytics for forecasting, and agentic AI for complex, variable, multi step workflows that require judgment and coordination.

How big are the potential productivity gains from agentic AI?

In security operations, Exabeam reports up to 80 percent faster investigations and 50 percent faster incident response. McKinsey finds that integrated platforms of reusable AI assets can remove 30 to 50 percent of nonessential work in some processes, and estimates that autonomous agents could drive around 93 billion dollars in workflow value by 2030.

How can Eaton & Associates support our AI and automation journey?

Eaton & Associates offers end to end support, including process assessment, workflow redesign, secure AI platform deployment, integration of generative and agentic AI with existing tools, and ongoing monitoring and optimization through our comprehensive IT consulting and managed services. We help you move from pilots to production with clear governance and measurable business outcomes.