AI Agents in SaaS, Healthcare, and Legal Services

AI agents use generative AI to work through a task. Salesforce's explainer on AI agents describes software that picks its next step and the tools it needs instead of following a script. An agent can read a request in plain language and act on it. It can pass a hard case to a person.

AI agents can work around the clock across industries. They understand human language and choose their next step for each request. Most do not retrain themselves between conversations.

Early open-source agents put this idea into practice. AutoGPT, released on March 30, 2023, breaks a user-defined goal into sub-tasks. It uses tools such as web browsing and file management to complete them.

A scripted chatbot follows fixed paths. An agent chooses its next step, which makes handoff rules and approval steps more important.

This guide covers AI agents in SaaS, healthcare, and legal services. It names the companies and sources behind the examples and explains how the agents work.

AI agents in SaaS: how companies use them

SaaS companies add AI agents to their platforms so customers and employees can request a result in plain language. The agent works inside a CRM or project management tool. Salesforce sells Agentforce for this purpose. Businesses can use it to build custom agents that handle customer inquiries and automate workflows. Those agents can also find insights in data. Salesforce describes service agents that resolve inquiries without a person, such as order-status questions or troubleshooting requests. Google Cloud offers Gemini for customer experience.

An agent can work on several tasks at once. A handoff rule can tell it when to pass a complex issue to a person.

• Customer support. In SaaS apps, AI agents can handle support requests around the clock. An agent resolves common issues and answers FAQs without a person, which leaves support teams more time for specialized cases. When an issue is too complex, the agent passes the full context to a person, so the customer does not repeat the story. Salesforce's explainer says supervisor agents can escalate to a human when needed. It also says an agent should require explicit human approval before a high-stakes or irreversible action. A support agent works best with a written rule for cases it cannot close. The rule names the topics that go to a person and the context the agent passes along.
• Sales and marketing. A CRM agent can use customer data to suggest the next step on a deal. It can draft personalized follow-up emails from past interactions or generate promotional content. It can recommend which leads to call first or write a follow-up that addresses a client's needs. A person reviews anything sent under the company's name. Track how often reviewers edit drafts to see whether the agent is improving.
• Work across apps. Agents can connect several SaaS tools. A user can request a task in plain language instead of clicking through different software. One example is "find all tickets about issue X and draft a summary email." The agent breaks the request into steps and searches knowledge bases. It pulls information from several apps through connectors and returns one result. Google announced Agentspace in December 2024 as a company-branded search agent that works across a company's documents and email. Its pre-built connectors cover Confluence, Google Drive, Jira, Microsoft SharePoint, and ServiceNow. Google launched Gemini Enterprise in October 2025.

Searching across apps can reduce the number of tools workers need to open. Google's announcement cites its research on enterprise workers. Google says they use an average of four to six tools to ask and answer a question. One agent that searches those tools shortens the process. The agent should read only sources the person asking is allowed to see.

SaaS agents run on large language models (LLMs) connected to company data. The model uses natural language processing to read a request. Its machine learning training shapes the next step it chooses. Many follow the ReAct pattern. IBM defines a ReAct agent as one that combines chain-of-thought reasoning with external tool use. ReAct stands for "Reasoning and Acting." The agent reasons about a request and calls a tool. It uses the result to choose its next step, so it can plan tasks that take several steps. Each tool call can introduce a mistake, so keeping logs of those calls helps.

AI agents in healthcare: how staff and patients use them

Hospitals test AI agents for patient communication and clinical reference work. The named examples below come from Google Cloud's list of generative AI use cases from its customers. Google last updated the list in April 2026. The unnamed uses are general examples. Agents support doctors and nurses, while clinicians make medical decisions.

• Patient support. Patients can use an agent at any hour to ask routine questions or get help scheduling appointments. Agents can also send medication reminders. An agent uses natural language processing to understand health questions and draws answers from approved medical sources. It can help triage symptoms by asking questions and suggesting a next step. Patients get timely answers when staff are busy or the office is closed. Medical questions outside the agent's scope must go to staff, so handoff rules matter as much as answers. Start with reminders and scheduling, because a wrong answer costs little there.
• Clinical assistance. Google Cloud's list says HCA Healthcare is testing Cati, a virtual AI caregiver assistant. Cati supports continuity of care between shifts. The list says HCA also uses generative AI for clinical documentation. Agents can draft patient visit summaries and fill in electronic health records by transcribing and organizing information. Sami Saúde is a healthcare network in Brazil. Google Cloud's customer use cases say AI generates its patient summaries.
• Data analysis and diagnostics. Healthcare produces large volumes of data, including lab results and imaging scans. Research papers add to that volume. Agents use machine learning to find patterns people may miss, such as rising blood pressure readings in a patient's history. An agent could study past cases to flag patients at risk of complications after surgery. An early flag could help the care team step in sooner. Google Cloud's list says Hospital de Amor uses Google Cloud AI to analyze X-ray images for early lung cancer detection. These tools give doctors a second opinion, and the doctor makes the final call. Agents can also match patients to clinical trials by comparing eligibility criteria with health records. Doing that by hand is slow.
• Clinical guideline search. Google Cloud's list says Seattle Children's Hospital built Pathway Assistant, which lets pediatricians search thousands of pages of clinical guidelines. The tool finds guidelines, and the pediatrician decides how to use them. Showing the source guideline beside each answer lets the pediatrician check it.

Healthcare agents rely on NLP and LLMs, with specialized medical models and expert feedback during training. An agent pairs a language model with approved sources, such as clinical guidelines or a patient's record. Patient data carries privacy obligations, so access controls determine which records an agent can read. A clinical pilot also needs a named clinician to review the agent's output and approve its scope.

AI agents in legal services: how teams use them

Legal work often involves long documents and slow research. Agents can help firms and legal departments work faster. They can read and draft documents and search documents and laws. Lawyers review the output before it reaches a client.

• Research and document review. Google Cloud lists a Cognizant agent that helps legal teams draft contracts. Cognizant built it with Vertex AI and Gemini. The agent assigns risk scores to contract clauses so a lawyer can read the riskiest ones first. It suggests edits to clauses it scores as high risk. Document extraction tools pull key information, such as parties and dates, from long contracts. They can also identify obligations. Lawyers can then spend more time on strategy and client advice.
• Client service. Salesforce's article on AI in the legal industry says Freshfields uses Salesforce's CRM and AI to centralize client data. It says Clifford Chance uses AI to make compliance reporting for clients more efficient. Both claims come from Salesforce marketing content and reflect the vendor's account. Legal agents can also answer routine client questions, such as case status or the next hearing date. They pull answers from case management systems, so clients get quick updates at any hour.
• Decision support and risk analysis. An agent can search statutes and past cases. For example, it could answer "Has this court ruled on this issue before?" by returning relevant precedents. A contract agent that assigns risk scores, as in the Cognizant example, gives a lawyer a ranked list of clauses to read first. The lawyer decides which risks matter.

A legal agent works from a firm's documents and approved legal sources. It uses language models trained on legal texts and retrieval systems that reach selected legal databases. Client confidentiality shapes where the agent runs and who can see its output. When needed, agents run in private cloud environments or on-premises systems to protect client data. Test a contract agent on a sample of the firm's own contracts before relying on it.

Start with one AI agent pilot

AI agents can help teams with repetitive, document-heavy work. Define the pilot's scope and decide when to stop if results fall short.

• Choose a task. Find repetitive, data-heavy work, such as customer queries or document review. Start with one pilot project. Before it begins, write down how you will judge success, such as time to resolution or edits per draft.
• Choose the tools. A platform that already connects to the team's systems can shorten the pilot. Salesforce's Agentforce and Google's Gemini Enterprise are two examples.
• Train the agent. An agent works from the documents it receives, such as knowledge base articles and policy documents. Many systems let you securely tune the agent on your own data. Have your team review early outputs and correct mistakes. Their review shows where to adjust the agent's instructions and source documents.
• Review pilot results. Begin in one area and track performance metrics. Widen the scope as results hold up.
• Set team habits. Name the tasks the agent should never handle alone so the team knows where its judgment applies. Encourage employees to see the agent as a colleague rather than a threat. When an agent takes over repetitive work, people have more time for creative and strategic work. Train employees to work with it, including how to phrase requests. Sharing quick wins, such as a problem the agent solved or time it saved, can build buy-in.

The pilot should explain how the team will switch the agent off.

A pause switch and a named owner let the team stop the agent quickly.

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