A practicalguide toAI agents
Key agentic AI concepts, use cases and considerations to drive ROI.
AI agents are like computer programs that can perform tasks independently using artificial intelligence. Their potential is massive, but the quality of their work depends on the data they use.
AI agents are like computer programs that can perform tasks independently using artificial intelligence. They can significantly boost business productivity by handling complex, multi-step operations quickly. To work effectively, these agents need access to an organization's data, which can be both structured data — patient records at hospitals or transaction details at a department store for example — and unstructured data, like video files or social media posts.
The potential for AI agents is massive but the quality of work depends heavily on the data it uses and how accurately it can find the right information. Organizations often struggle with this because how AI understands data doesn't always match how the data is stored.
Still, AI agents are expected to become a valuable resource in the workforce, helping with roles like customer support, field technicians, data analysis and more. It's also important to understand that there are two different types of AI agents: personal and company.
Both types of agents will free time for employees to focus on more important and complex tasks. Specialized AI agents are particularly useful because they can combine data and tools to provide more accurate and reliable insights by finding and extracting answers from the best data sources for each task.
For AI agents to be widely used in businesses, they need secure access to company data and a way to manage that access, similar to how employee access is managed. They must follow company rules about data, efficiently use different data sources, and find accurate information to deliver valuable results.
However, deploying these agents at a large scale presents challenges, such as ensuring the accuracy of their outputs, maintaining security and trust, and managing access to diverse data sources.
Company AI agent
- Serves
- Organizational goals, often many users
- Data
- Shared company data
- Learns from
- Business parameters and outcomes
- Rules
- Corporate policies and access controls
This ebook gives an introductory overview of agentic AI — AI designed to function autonomously — how it differs from traditional and generative AI, the benefits it brings, a strategic roadmap for implementation, and real-world use cases across industries.
Generative AI
Creates content and communications personalized to a customer’s inquiries by analyzing large data collections and adapting tone and style to brand guidelines or customer needs.
AI agents
Act as active problem solvers that can handle interactive and multi-step engagements with customers using a natural language interface.
Same guide, same interactives. Pick how much reading you want around them — you can switch any time from the bar at the top.
Chapter summaryWritten by Anthropic Claude 3.5 Sonnet
- Agents run multi-step work — like a root-cause analysis — by combining tools with private data.
- Only 10% of large enterprises use them today; 82% plan to within three years.
- Nearly every decision mixes structured and unstructured data — that’s where agents earn ROI.
AI agents capable of completing complex tasks without human intervention are quickly emerging as a major paradigm shift for businesses to drive a new wave of productivity, automation and creativity. These autonomous, goal-oriented systems are capable of more complex, multi-step tasks such as performing a and generating a report by accessing data and leveraging different tools. For these advanced analytical workflows with many edge use cases, access to private data — whether it be database or PDF files stored in object storage — is key.
AI agents can bring ROI to every part of the organization to accelerate existing workflows or enable new ones where previously technical talent gaps prevented more users from being part of data-driven decision-making. For most organizations, data is their core differentiator and being able to use that data effectively in decision-making is not a new goal — rather, it's become a more clear necessity.
Many companies have already made investments: in a survey of 1,100 large-enterprise executives, 10% of organizations currently use AI agents, more than half plan to use them in the next year, and 82% plan to integrate them within the next three years.
Most decision-making requires multiple data points and types. AI agents are replacing manual data stitching with automated, accurate insights — boosting productivity and enabling faster strategic decisions that ultimately will help unlock new revenue opportunities.
One thing to keep in mind as you start the journey to give employees a : delivering accurate answers while ensuring data privacy becomes increasingly complex as more data sources are connected and more AI agents are built on top of them. To address this from the start, evaluate solutions with a platform mindset where shared data governance, processing and retrieval resources drive cost-efficiencies.
Five benefits for early adopters
Companies that leverage the opportunities, mitigate the risks and adopt early are poised to reap substantial benefits. Open each for the detail.
01Enhanced efficiency and cost savings
Automating routine and time-consuming tasks and streamlining workflows.
02Improved decision-making
More accurate insights and real-time adaptability.
03Speedier time to market
The ability to process information and carry out tasks in a fraction of the time.
04Higher customer satisfaction
Better retention through personalized, more responsive support.
05More opportunities for innovation
Freeing up workers to focus on more complex and creative tasks.
Chapter summaryWritten by Anthropic Claude 3.5 Sonnet
- LLMs are passive; agents plan, act and learn — from passive responder to active participant.
- Every agent runs a six-step loop: sense, reason, plan, coordinate, act, learn.
- Data agents live or die on three things: accuracy, efficiency and governance.
The evolution from LLMs to AI agents
LLMs laid the groundwork for natural language understanding and created the ability for AI to process and generate text based on patterns in existing data. However, they are fundamentally passive and cannot take direction or respond adaptively. They have no decision-making or autonomous capabilities.
AI agents fill these gaps by expanding the role of AI, from passive responder to active participant. They combine both approaches by using gen AI capabilities within autonomous systems that can plan, reason and take actions to achieve specific goals.
This shift potentially marks the beginning of more versatile and independent artificial intelligence systems. Let’s take a look at how AI agents work.
Micro-video slot — a silent 8–12 s loop (MP4/WebM, muted, playsinline) drops into this frame; shown here as a live CSS/canvas loop.
Where enterprise value lives
While general-purpose AI assistants play a key role in our personal lives — who doesn’t want to automate flight search for the next vacation — in the enterprise, accessing internal data is what drives value for people.
Data agents are a category of AI agents focused on efficiently combining data and tools to deliver data-grounded insights with an extreme focus on the accuracy of the results — because the decisions business teams make on the data can be the difference between making or losing millions.
Just like with any type of agent, specialization is key, and these will need to be scoped to specific business functions and units. Sales teams may have a different data agent than those working in research and development.
AData retrieval
Plan and route requests to the correct structured or unstructured source — and enforce data access and governance policies while doing it.
BCalling tools or other agents
Combine retrieval outputs with other tools — to generate a histogram or create a report — or collaborate with other agents that have access to more tools.
COutput delivery
Send a message through chat, write results to a table that feeds a dashboard, or email a PDF report with asset performance, portfolio updates and market sentiment for each fund manager.
Without data, there are no data agents
A team of developers could have great tools to generate charts or reliable integrations between agents and downstream systems, but without the correct data, what are those good for? With data retrieval being foundational, here are three critical elements to address. Tick each one your organization has covered — they count toward your readiness score.
Three elements decide whether a data agent is trustworthy. Tick each one your organization has covered.
From an architectural perspective, the primary tools needed are retrieval tools for structured and unstructured data — and they must have access to data that is properly prepared, with governance and access controls in place.
The diagrams above oversimplify how a single agent works. In reality — similar to business structures — there need to be “managers” and “cross-functional teams” for AI agents as well. Multiple agents can work together to process account changes, resolve billing issues and provide personalized incentives by analyzing information scattered across databases, web pages and emails to discern the most reliable, up-to-date information.
Then they reason — analysis, pattern recognition and decision-making algorithms generate options. After choosing the best option, they coordinate the plan and execute it. Finally, they learn from the results and improve for the next time. At the same time, they bring up a host of challenges and ethical implications that need consideration.
Chapter summaryWritten by Anthropic Claude 3.5 Sonnet
- Risks start with data privacy and algorithmic bias, then integration, compute and governance.
- Guardrails, evaluation and observability keep agents inside ethical norms.
- Agents must know when they’re uncertain — and hand off to a human.
Organizations thinking about deploying AI agents face significant ethical and technological challenges. Ethical concerns start with data privacy and , because these systems can process sensitive information without the proper context and then perpetuate existing societal inequalities.
Technical challenges must also be overcome, including ensuring AI systems remain reliable and consistent in their decision-making, particularly when faced with unique situations not built into their training data. Integration with legacy systems and existing workflows can also be difficult; organizations must carefully manage the transition to AI-enhanced processes while maintaining operational continuity.
Organizations also have to be prepared to maintain transparency in their AI systems’ decision-making processes. This is critical to building trust with users and meeting regulatory requirements.
For AI agents to work at scale, they need a secure connection with enterprise data and unified governance to manage their access, similar to existing controls for your employees. They must follow data policies, access multiple sources efficiently and retrieve accurate information to deliver reliable, high-value outcomes.
Integration, compute, security and governance
Open each challenge for the detail.
01Integration with data and other systems
Agents need to integrate seamlessly with existing data ecosystems and enterprise systems — challenging because of differences in data formats, silos and interoperability issues.
02Compute infrastructure
Agents need vast compute, often demanding significant investment in GPUs or cloud resources, to access, process and analyze real-time data efficiently.
03Security and governance
Like employees, agents must comply with data privacy regulations while preventing unauthorized access or data leakage. Strong governance policies monitor model behavior, manage access controls and mitigate biases, keeping AI aligned with business objectives and regulatory standards.
Organizations that deploy AI agents must implement robust evaluation frameworks to continuously test them against fairness, bias and safety criteria, and ensure real-time monitoring so models don’t stray from established ethical norms. They must adopt transparent and accountable AI governance strategies, including exercises, diverse data audits and clear escalation paths when AI actions violate their ethical guidelines.
Appropriate handoffs and feedback
AI agents are not standalone solutions that can be turned on and trusted to work on their own. They work best when designed to work with humans. A major challenge is defining when and how AI agents should hand off tasks to humans, especially in high-stakes scenarios such as customer service, healthcare or financial advisory.
Smooth transitions require agents to detect when they are uncertain, recognize complex queries beyond their capabilities, and escalate appropriately. Continuous improve AI performance over time — though organizations often struggle to collect, analyze and implement this feedback efficiently.
Opening the black box
AI agents often function as , making it difficult to understand how decisions are made. This lack of transparency erodes trust, especially in regulated industries like finance, healthcare and law.
Agents need to provide clear rationales for their decisions, highlighting key data points and reasoning pathways. But balancing explainability with performance is hard: more-transparent models may sacrifice accuracy or efficiency.
Agents themselves can help by generating human-readable explanations, identifying biases and ensuring compliance. Achieving this requires frameworks alongside automated audit-log reviews and manual reviews where models explain the steps they took. As review processes mature, AI can automate and scale explainability.
Chapter summaryWritten by Anthropic Claude 3.5 Sonnet
- Five roadmap moves: foundation, business goals, communication, change management, security.
- Five architecture principles: scalability, flexibility, data accessibility, trust, security.
- Tick what’s true for you — this chapter drives your readiness score.
A strategic roadmap
Start with a foundation that can carry the load
AI agents can be a game changer for the enterprise. But for successful deployment and ROI, company leaders need to start with a solid foundation that can handle its significant resource demands.
Chapter summaryWritten by Anthropic Claude 3.5 Sonnet
Agentic outputs are only as good as the quality of the underlying data and the accuracy of the retrieval systems that help ground them. Yet organizations struggle to pave a path to production due to an AI and data mismatch.
LLMs excel at unstructured data, but many organizations lack mature preparation practices for this type of data; meanwhile, structured data is better managed, but challenges remain in enabling LLMs to understand rows and columns (the text-to-SQL task).
Snowflake customers now have a unified platform for processing and retrieval of both structured and unstructured data with high accuracy out of the box. End-to-end unified governance, from ingestion to application, enables teams to deliver a new wave of data agents — scalable solutions that enforce access and privacy controls.
“Traditional data management methods are increasingly insufficient given the exponential data growth. Many enterprises face overwhelming data sources, from structured databases to unstructured social media feeds. Manual processes can be time-consuming and error prone. Agentic AI automates these processes, helping ensure data integrity and offering real-time insights. Leveraging advanced machine learning and natural language processing, these intelligent agents can efficiently manage and analyze vast data amounts. The integration of Snowflake’s AI Data Cloud and launch of Cortex Agents, along with Deloitte’s experience, can optimize these processes for efficiency and innovation.”Jim RowanHead of AI, Deloitte Consulting LLP
“At Luminate, we're revolutionizing how we deliver precise, data-driven insights to our clients through generative AI applications. Snowflake's unified data and AI platform provides our developer team with scalable processing and retrieval for both structured and unstructured data — the critical building blocks for developing, deploying and orchestrating data agents powering our applications. Using Snowflake Cortex AI brings advanced AI within the same security and governance perimeter as our data and saves us countless development hours, allowing us to unlock the full potential of entertainment industry data with agentic AI.”Glenn WalkerChief Data Officer, Luminate Data
Six quick questions. Every answer explains itself, and each correct one adds a key idea to your recap deck.
Large enterprises are dispatching AI agents across departments. Agents continuously monitor and analyze streaming data, and decide, plan and execute in real time.
Customer service
issues resolved per hour at a large call center, while handling time fell 9%.
Sales & marketing
prospecting time, with real-time guidance boosting conversion rates and deal times.
Finance
from automating repetitive tasks like invoice processing, plus real-time insights for growth decisions.
IT
Assistants write and debug code, monitor systems, resolve tickets and generate reports.
Accenture and Snowflake’s role in facilitating agentic AI
Accenture used Snowflake’s platform to create an AI insurance claims agent that reviews documents, summarizes information, makes claims decisions and generates personalized letters explaining the reasoning behind an approval or denial.
01What’s under the hood
“Underlying the Gen AI Claims Agent is Snowflake’s AI Data Cloud, which includes capabilities like Document AI that can process various documents, such as driver’s licenses, receipts and forms. We also use Snowflake’s Cortex Analyst, which helps the AI Claims Agent analyze data and policy documents to make informed decisions.” — Mike Lao, Accenture data and AI team
02Why claims first
Claims approval doesn’t always require 100% accuracy — clients often accept partial payouts in exchange for faster processing, and decisions can be appealed internally. That contrasts with financial advice, which demands fiduciary responsibility. Faster payments help both the insurer’s bottom line and customer satisfaction.
03A human in the loop
“Keeping a human in the loop is important. We are using AI to reinvent work processes to scale AI adoption in organizations and meet client expectations.” — Mike Lao
04What comes next
The Accenture team in Manila brings deep claims expertise to global insurers; this is a first step toward broader adoption across financial services.
“In the future, we expect to see AI agents assisting with financial planning and personal investment advice. However, this will take time, as both technical solutions and regulatory acceptance evolve.” — Kaushik GD, Snowflake’s Head of Financial Services in APJ
Whether you’re just starting to experiment with generative AI or already have your first AI applications in production, Snowflake enables you to extend the benefits of AI to your entire organization by streamlining development, reducing complexity and helping secure your data every step of the way.
About Snowflake
Snowflake makes enterprise AI easy, efficient and trusted. More than 11,000 companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here.
Learn more at snowflake.com (NYSE: SNOW)
© 2025 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s).
ColophonHow this interactive edition is built
Pattern map
| Pattern | Where |
|---|---|
| Accordions / click-to-reveal | Ch 1 benefits, Ch 2 key actions, Ch 3 challenges, AI chapter summaries, Accenture case |
| Micro-quiz | Checkpoint — 6 questions, instant feedback, recap deck |
| Data widgets | Adoption waffle, department filter, access-scope trade-off, handoff threshold |
| Read-aloud | Introduction — word / sentence tracking, optional device voice |
| Process diagrams & loops | Agent loop (Fig 1), data-agent simulator (Fig 2–3), framework (Fig 4), micro-video slot |
| Ambient soundscape | Floating control — generated in-browser with Web Audio |
| Footnote popovers | Dashed terms throughout |
| Branching track | Header switch + “Choose your track” |
| Reading time | Per chapter, total per track, minutes left |
| Marginalia | Author reflections + community notes |
| Share / discuss | Select any text, popular highlights, quotes, stats, poll |
| Personalization | Settings drawer — size, theme, dyslexia type, bionic emphasis, calm motion |
| Gamification | Progress bar, 16 readiness checks, badges, result tier |
Palette (light)
| Token | Hex | Use · contrast |
|---|---|---|
| Snowflake blue | #29B5E8 | Panels, graphics; midnight text on it 7.0:1 |
| Blue ink | #0A6FA6 | Headings, links on paper 5.2:1 |
| Midnight | #0B1F33 | Buttons, quiz, player |
| Mid-blue | #11567F | Depth, data accents |
| Valencia orange | #FF9F36 | Highlights, unstructured data, packets |
| Ink | #10202F | Body text on paper 15.8:1 |
Type
Montserrat 800 (display, uppercase) · Lato (body) · JetBrains Mono (labels) · Atkinson Hyperlegible (dyslexia mode). Fonts fall back to system stacks offline.
Content: Snowflake, “A Practical Guide to AI Agents” (2025). Widget data marked illustrative is a demonstration model; community notes, reader counts and poll results are placeholders for a real community backend.