AI agents are software programs that work on their own using large language models (LLMs) to understand, plan, and do tasks without needing the same instructions over and over. Unlike regular chatbots or AI assistants that only answer when asked, AI agents can take complex commands and break them into smaller tasks to finish on their own.
In healthcare, AI agents can handle regular but important front-office jobs like scheduling appointments, answering patient calls, checking insurance coverage, and managing prescription refill requests. These jobs take time that could be used for patient care, clinical notes, or solving harder problems.
Research by IBM and Morning Consult shows that 99% of developers making AI applications for companies are working on AI agents in 2025. They want to use AI to do repetitive tasks faster and reduce human mistakes in busy settings. But AI agents can’t make all complex decisions alone—people still need to check their work to keep things safe and follow rules.
Open source AI models give a base to create AI agents that can be changed to fit what an organization needs, including healthcare settings. Developers can customize the AI so it works well with the rules and ways medical offices operate.
In places with weak internet like rural clinics, small medical offices, and community health centers, open source AI models have an advantage. They can be set up to work well even without a fast internet connection. This means these places don’t have to rely on cloud services that need strong bandwidth, which many rural places don’t have.
Google Cloud’s Vertex AI Platform is one example. It gives access to many open source and private models in a managed setup. It helps build AI agents that work even with less resources. Tools like Vertex AI Studio and Agent Builder let developers make AI programs with little coding. This is good for medical practices with small IT teams that want AI help.
Healthcare places in many parts of the U.S., especially in the countryside, often have few resources. They might not have advanced IT systems, small budgets for new technology, and slow or spotty internet. AI tools need to be made to work well within these limits.
Using open source AI models such as Llama 3.2 on platforms like Vertex AI, developers can make AI agents that do not need constant online connection to strong servers. Instead, these AI agents can run on smaller models that are still able to do important jobs like:
By working inside these technical and operational limits, AI agents make automated services easier to use without needing a lot of bandwidth or expensive setups.
AI agents do more than answer calls—they can fit into workflows to make office work run smoother. AI orchestrators manage many AI agents working together. They handle tasks like appointment scheduling, patient communication, and insurance checks.
In 2025, AI orchestrators are expected to be very important in big companies and healthcare offices. They connect different AI agents to give joined responses based on what patients or staff need. For example, one AI agent can handle a patient call to book an appointment while another checks insurance at the same time. The orchestrator keeps these actions going smoothly to save time and effort.
These AI-driven workflows help reduce the work on healthcare staff by automating repeated simple tasks. This lets people focus on more important work like helping patients or dealing with tricky insurance problems.
But healthcare providers should use strong governance rules when adopting AI. These rules make sure there is accountability, traceable records, and that healthcare laws like HIPAA are followed. Logs and ways to undo errors protect sensitive patient data during automated tasks.
Even though AI agents show promise, they are still early in development for full independence and difficult decision-making. Leaders in medical offices should know about some challenges:
Medical office managers and IT staff need to think about these points when picking and using AI automation tools.
In real life, AI agents made with open source models are useful for small medical offices and clinics that serve communities with fewer resources. They make many tasks easier and fit within the technology these places have.
Phone calls are still the main way patients reach doctor offices. Using AI agents to handle calls cuts wait times, stops missed calls, and improves patient experience. AI systems can understand natural speech, send calls to the right people, and save details for staff to follow up.
When open source AI models are made to work well with low bandwidth, these AI agents stay reliable even without fast internet. This keeps them working all the time, especially in remote areas where broadband is not steady.
AI agents help book, change, or confirm appointments using voice or text. They also manage referrals between primary care doctors and specialists. These agents work with calendar systems and insurance databases to reduce manual errors and speed up patient care.
Manual work for insurance checks and billing questions takes lots of admin time. AI agents that link with insurance databases can do eligibility checks and reply in real time to patients and staff. Custom AI models can work with many insurance plans across different U.S. states, which is important for practices serving different groups.
Using AI agents well needs a careful plan. Healthcare groups should:
Tools like Google Cloud’s Vertex AI include Model Garden and Agent Builder, which make it simpler to customize models and build AI apps, even for teams that do not have much coding experience. These tools help healthcare leaders bring AI into practice faster.
Open source AI models offer strong options to make AI agents for healthcare providers in areas with limited resources and slow internet in the U.S. By focusing on adaptable and lightweight AI tools and adding them into connected workflows, medical offices can automate normal front-office tasks without risking data safety or losing control.
Bringing in AI agents takes careful planning, good governance, and keeping humans involved to stay safe and follow rules. When done thoughtfully, these AI tools can improve patient experience, lessen admin work, and help healthcare groups deliver care more smoothly, especially in places where technology access is still a problem.
An AI agent is a software program capable of autonomous action to understand, plan, and execute tasks using large language models (LLMs) and integrating tools and other systems. Unlike traditional AI assistants that require prompts for each response, AI agents can receive high-level tasks and independently determine how to complete them, breaking down complex tasks into actionable steps autonomously.
AI agents in 2025 can analyze data, predict trends, automate workflows, and perform tasks with planning and reasoning, but full autonomy in complex decision-making is still developing. Current agents use function calling and rudimentary planning, with advancements like chain-of-thought training and expanded context windows improving their abilities.
According to an IBM and Morning Consult survey, 99% of 1,000 developers building AI applications for enterprises are exploring or developing AI agents, indicating widespread experimentation and belief that 2025 marks the significant growth year for agentic AI.
AI orchestrators are overarching models that govern networks of multiple AI agents, coordinating workflows, optimizing AI tasks, and integrating diverse data types, thus managing complex projects by leveraging specialized agents working in tandem within enterprises.
Challenges include immature technology for complex decision-making, risk management needing rollback mechanisms and audit trails, lack of agent-ready organizational infrastructure, and ensuring strong AI governance and compliance frameworks to prevent errors and maintain accountability.
AI agents will augment rather than replace human workers in many cases, automating repetitive, low-value tasks and freeing humans for strategic and creative work, with humans remaining in the decision loop. Responsible use involves empowering employees to leverage AI agents selectively.
Governance ensures accountability, transparency, and traceability of AI agent actions to prevent risks like data leakage or unauthorized changes. It mandates robust frameworks and human responsibility to maintain trustworthy and auditable AI systems essential for safety and compliance.
Key improvements include better, faster, smaller AI models; chain-of-thought training; increased context windows for extended memory; and function calling abilities that let agents interact with multiple tools and systems autonomously and efficiently.
Enterprises must align AI agent adoption with clear business value and ROI, avoid using AI just for hype, organize proprietary data for agent workflows, build governance and compliance frameworks, and gradually scale from experimentation to impactful, sustainable implementation.
Open source AI models enable widespread creation and customization of AI agents, fostering innovation and competitive marketplaces. In healthcare, this can lead to tailored AI solutions that operate in low-bandwidth environments and support accessibility, particularly benefiting regions with limited internet infrastructure.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
