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AI Agents

AI agents are software systems that use generative AI to perform tasks with a degree of autonomy and agency.​
Squid AI enables you to connect your existing systems and data with a built-in retrieval-augmented generation (RAG) engine and the LLM of your choice to create purpose-built AI agents and workflows for your business.​

Understanding AI Agents​

What are AI Agents?​

The term "agent" in AI agents refers to the concept of agency - the ability to act or make decisions on behalf of a user or organization, and distinguishes AI agents from traditional software, tools, and analytical systems. Unlike these, AI agents can make decisions, take actions, and provide intelligent solutions across various enterprise contexts.

An AI agent operates with:

  • Purpose-driven intelligence - working toward specific business objectives
  • Contextual awareness - understanding data within its business environment
  • Action-taking capability - performing operations beyond just providing information
  • Learning and adaptation - improving performance over time

How Generative AI enables AI agents​

You may be wondering why AI agents have developed only recently, and while there are multiple factors, generative AI unlocks two key capabilities that were much harder, if not impossible, to do at scale before:

  • It allows us to interact with AI technology using conversational language, like talking to another person, making AI far more accessible.

  • It enables us to gain insights into large volumes of unstructured data (documents, text, and multimedia), which previously required forcing unstructured data into structured formats (databases, tables, etc.)

In short, AI agents are now much closer to understanding and interacting with data, systems, and people the way we already do naturally.

Comparing LLMs and AI Agents​

Understanding the distinction between Large Language Models (LLMs) and AI agents helps clarify where and how agents provide value in enterprise contexts.

Large Language Models: Capabilities and Limitations​

Large Language Models offer powerful natural language processing capabilities but face several constraints in enterprise applications:

  • Contextual limitations: LLMs are trained on general data and lack specific business context or industry-specific knowledge without additional customization
  • Data access constraints: Standard LLMs cannot directly access enterprise data sources or retrieve real-time information
  • Structured data handling: LLMs are primarily designed for natural language and have limited ability to process complex structured data accurately
  • System integration: LLMs cannot natively connect to existing enterprise workflows and systems without additional infrastructure and orchestration
  • Security and governance: Base LLM implementations typically lack enterprise-grade security controls and audit capabilities

How AI Agents Extend LLM Capabilities​

AI agents build upon LLM foundations by adding critical enterprise capabilities:

  • Business context integration: Agents can be configured with domain-specific knowledge and company information
  • Data connectivity: Agents incorporate connections to multiple data sources, both structured and unstructured (databases, APIs, documents, files, SaaS)
  • Cross-system functionality: Well-designed agents work across enterprise systems for comprehensive views and actions
  • Security and governance: Enterprise AI agents include proper security controls, permissions, access controls, and audit trails
  • Action capabilities: Unlike LLMs that primarily generate text or can take some limited actions on the behalf of individual users, agents can initiate actions and workflows across departments, systems, and organizations

The AI Agent Spectrum​

While no standard definition of "AI agents" exists across the market yet, we have found the following spectrum of capabilities and autonomy to be a useful framework:

LevelCategoryDescriptionKey Characteristics
L0No AI AutomationCurrent state - existing or manual processes using basic or current toolsRelies completely on human effort to understand and act; no automation
L1Assisted WorkGenerative AI that helps with brainstorming, writing, or searchingOften chatbots with limited context or trained on public data only
L2Augmented EnterpriseTake action, resolve issues, propose solutions with enterprise business contextWith or without a UI, and works across systems for a 360° view
L3Autonomous EnterpriseOperate independently with minimal to no human supervisionStill emerging, but represent the future of enterprise-wide AI

Most organizations today are focusing on L2 Augmented Enterprise agents, which provide significant operational value through their ability to call APIs and functions, access proprietary data, and perform actions while keeping humans in the loop.

Types of Enterprise AI Agents​

We've found breaking AI agents into three different categories to be an effective way to think about them. Knowledge agents augment your teams, system agents augment your systems, and data agents augment your knowledge and products.

Most enterprises will need to combine two or more of them together into one solution to address their real-world situations and use cases.

Knowledge Agents​

  • Purpose: Augment your teams by making them more productive
  • Function: With access to relevant data and systems across the organization, they provide insights, answer questions, generate content, and simplify information access
  • Example Use Case: Sales enablement AI agents that can instantly access product documentation, pricing, and competitive intelligence for a sales person during live customer calls.

System Agents​

  • Purpose: Augment your systems by connecting across them to automate processes and workflows
  • Function: Trained on policies, agents can also connect disparate systems and automate manual workflows
  • Example Use Case: Compliance agents that decrease invoice errors by verifying that specific line items on an invoice are accurate and in compliance with the company's billing policies. Errors or policy violations can be flagged, or remediation suggestions generated for human approval if necessary.

Data Agents​

  • Purpose: Augment your knowledge & products by making data accessible
  • Function: Extract insights directly from raw data, create rich data visualizations, and enable natural language interactions with your data
  • Example Use Case: Embedded analytics and natural language querying capabilities within products that serve as customer-delighting and revenue-unlocking features

How to start thinking about AI agent use cases​

Adopting a new technology can be daunting, but having the right mindset can be helpful. Here's how you can begin your AI agent journey.

Identify high-value use cases​

Focus on areas with clear ROI potential. Look for repetitive, manual internal processes, at junctions where two different systems meet and information is needed from both systems, or other scenarios where you might need information from more than one system or data source.

Limit scope​

Once you've identified a high-value use case, try to start with a small, self-contained piece, usually with an internal team. This will make it easier to try an AI agent at lower risk (vs. a customer-facing or external use case) and allow you to move faster to prove value.

Start with augmentation, not replacement​

Enhance human capabilities rather than attempting to fully automate roles. While the idea of fully autonomous agents grab headlines, AI agents today are best at augmenting your workforce and can already provide a boost in productivity at much lower risk than running fully autonomously.

Build for low disruption​

Envision how agents can work within your existing technology ecosystem and workflows to minimize disruption and the need to learn yet another tool.

Prioritize security and governance from the start​

Start thinking about who will be using your AI agent and what resources it will have access to right at the beginning and how agents should respond.

When you have a successful AI agent, the first thing people will want to do is to share it with others, and the same security, governance, and compliance requirements required of any enterprise software will apply to AI agents too.

By strategically implementing AI agents with appropriate governance and integration, organizations can achieve significant operational improvements, cost savings, and competitive advantages.

Next steps​

Ready to try building an AI agent? Squid AI's Agent Studio is the easiest place to start.

Explore more​