What are AI agents, and what can they do for healthcare?

These agents may not have the code name 007, but they can still offer powerful and compelling solutions for the pain points in healthcare.

A new frontier of AI is emerging as artificial intelligence moves from assistants to agents. Unlike AI assistants such as chatbots, which wait for human prompts and complete discrete tasks, AI agents can be configured to pursue goals and carry out entire workflows end to end—coordinating tools, people, and data autonomously within defined guardrails.

Healthcare faces rising costs and persistent workforce shortages, and that creates urgent demand for solutions that reduce administrative burden. AI agents in healthcare can perform routine, time-consuming tasks—verifying coverage, triaging scheduling requests, or compiling documentation—escalating only when human judgment is required. That enables clinicians and staff to spend more time on direct patient care and lets payers focus on delivering cost-effective, timely services to members.

In this article we explain how agents work in real-world care and back-office scenarios, give concrete examples (from patient scheduling to claims processing), and outline practical first steps for piloting agentic AI safely and effectively. Read on to see how AI agents can help your organization save time, reduce manual data entry, and improve the patient experience.

How healthcare can put AI agents to work

AI agents are software “workers” that combine generative AI, predictive models, and reasoning to complete multi-step tasks. Once given a goal, constraints, and access to tools and data, agents can pursue workflows autonomously—calling other systems, synthesizing information, and escalating to humans when guardrails require review.

Patient-facing tasks: improve access and experience

Agents in healthcare can help patients find the right site of care for nonemergency needs, book and reschedule appointments, collect previsit information, and send tailored instructions before and after visits. For example, an agent can triage a scheduling request by checking a patient’s insurance, matching availability across providers, and offering the best appointment slots—then confirm the booking and add an intake checklist. These capabilities reduce back-and-forth with call centers and free staff to focus on higher-value patient care and complex cases.

Care coordination and clinical workflows

During care episodes, agents can coordinate case management tasks across teams and sites—assembling relevant patient data, prompting clinicians about care gaps, and scheduling follow-ups. For instance, after discharge an agent can generate a tailored care summary, check medication instructions against the patient’s records, and proactively schedule a follow-up visit or home health referral if indicated. By automating routine coordination, agents help improve patient outcomes and reduce readmissions.

Administrative automation: billing, claims, and networks

Multiagent systems are especially effective for back-office workflows that span organizations. Consider the claims lifecycle: after a patient encounter, providers prepare an itemized bill and submit a claim. Agents can verify insurance eligibility, suggest the correct billing codes, flag documentation gaps, and compile the submission for clinician review. On the payer side, agents can perform coding checks, collect supporting documents, synthesize clinical information for reviewers, calculate payments, and generate the 835 transaction used to populate providers’ payment systems and the Explanation of Benefits sent to members. When discrepancies show up, another agent can assemble an appeal draft for human approval. Automating these steps can accelerate reimbursement, reduce manual data entry, and cut administrative time for both providers and payers.

Complex tasks and multiagent coordination

A fleet of specialized agents—each assigned roles such as orchestration, task execution, review, or planning—can cooperate to complete complex workflows. For example, a contracting use case can assign one agent to surface negotiation priorities and another to draft contract language, with an orchestration agent sequencing reviews and flagging items for human negotiation. Similarly, in call centers agents can retrieve authoritative answers, populate forms, and complete scheduling tasks while routing unresolved queries to staff. These systems improve throughput across provider networks and payer operations.

Risks, privacy, and human oversight

Agents often need access to patient data to perform effectively, which raises privacy and compliance requirements (for example, HIPAA in the U.S.). In clinical or high-stakes decisions, a human-in-the-loop checkpoint is essential: agents should escalate ambiguous cases, flag clinical‑risk items, and log decision rationales for audit. Implement governance controls, role-based access, and “tools” that constrain agent actions (for instance, preventing deletions of clinical records) to reduce operational and safety risks.

Evidence and measurable benefits

Early deployments suggest agents can deliver measurable time savings—reducing scheduling workloads, cutting claims processing time, and lowering administrative overhead—though exact gains depend on the use case and data quality. Where possible, pilot programs should track KPIs such as time-to-schedule, claims turnaround, appeals rate, staff time reclaimed, and changes in patient experience to quantify benefit and build the case for scale.

Next steps: start with a narrowly scoped pilot (for example, automated scheduling or claims triage), involve clinical, IT, compliance, and call-center stakeholders, and define success metrics up front. Successful pilots can expand into multiagent workflows that connect care teams, payers, and patients—unlocking broader efficiencies across the healthcare industry.

How to get started with implementing agentic AI

Implementing agentic AI provides an opportunity to rethink how work gets done across the healthcare industry. Rather than scattering pilots across unrelated areas, organizations should target a few high‑impact domains and build reusable foundations so benefits compound as use cases scale. Below are six practical considerations—each with clear next steps—to help teams move from experimentation to sustained value while protecting patients and data.

1. Ascertain whether agents are the right solution

Not every problem needs agentic AI. Use traditional software when a task is a one‑off or rule-based (for example, password resets) and conventional AI or predictive models when the task is well-defined and driven by explicit inputs (for example, forecasting surgery duration). Agentic AI is best when you need an end-to-end workflow that connects multiple systems, ingests evolving inputs, reasons over information, and takes sequenced actions—such as coordinating patient transitions across care settings or orchestrating complex claims adjudication.

• Next steps: map the target workflow, list systems and data sources, and estimate frequency and variability of inputs.
• Decision trigger: choose agents when the workflow requires dynamic tool use, information synthesis, and conditional escalation to people.

2. Get the strategic fundamentals right

Prioritize pilots by potential business impact and operational pain points—for example, scheduling bottlenecks that consume clinician time or claims processes that cause delays and underpayments. Avoid “pilot purgatory” by setting a clear scope, owners, and success metrics from day one. Adopt a “focused transformer” approach: start with a few domains, demonstrate measurable wins, and use those to fund broader rollouts.

• Next steps: define KPIs (time saved, claims turnaround, appeals reduction, patient satisfaction) and a 90–180 day pilot plan.
• Stakeholders: include clinical leaders, IT/architects, compliance, revenue cycle, and call-center managers.

3. Make the right architectural choices

Design architecture based on complexity: simple, single-purpose agents for low-risk tasks (routing requests, filling forms) and multiagent systems for complex, cross-system workflows. Multiagent architectures assign roles—such as orchestration, task, review, and planning agents—to coordinate activity efficiently and enable scale.

• Technical notes: separate orchestration logic from skill agents; use secure connectors to EHRs, scheduling systems, and payer portals; and instrument robust logging for traceability.
• Next steps: build a minimum viable agent that integrates with one or two systems (e.g., scheduling + eligibility) and validate end‑to‑end flows.

4. Manage risks and enforce governance

Agent autonomy requires strong governance: define human‑in‑the‑loop checkpoints for clinical or high‑risk decisions, maintain auditable logs of agent reasoning, and restrict agent capabilities through tool-level controls (for example, preventing record deletion). Ensure all data access complies with HIPAA and local regulations and implement role-based access and consent management where appropriate.

• Next steps: create an approval matrix that lists which actions an agent can take autonomously and which require human sign‑off.
• Audit: instrument decision logging and periodic review of agent outputs for bias, accuracy, and safety.

5. Invest in change management and new talent

Agentic AI changes how people work. Expect shifts in daily tasks, with routine data entry and scheduling handled by agents while staff focus on higher-value clinical and coordination work. Address workforce concerns proactively: provide upskilling for clinicians and revenue-cycle staff, hire few specialists in AI/ML and orchestration design, and create clear career pathways tied to new workflows.

• Next steps: run stakeholder workshops, develop role-based training, and publish new operating procedures that show how humans and agents collaborate.
• Measure adoption: track time reallocated from administrative tasks to patient care and collect staff feedback.

6. Consider build versus buy—and openness

Decide whether to build agentic systems in-house or partner with vendors and startups. Enterprise architects should evaluate how agent solutions fit your tech stack and whether systems should be open (interoperable across vendors) or closed (tightly integrated with legacy systems). For many organizations, a hybrid approach—custom orchestration with vendor-provided skill agents—balances control and speed.

• Next steps: run a short vendor bake-off, test connectors, and evaluate TCO including maintenance, retraining, and governance costs.
• Consider interoperability: prefer APIs and FHIR-based integrations to reduce future vendor lock-in.

Practical pilot checklist (90–180 days): define scope and KPIs; secure data access and compliance sign-off; assemble stakeholders (clinical lead, revenue cycle, IT, compliance); develop an MVP agent that automates 1–2 tasks; instrument metrics (time saved, claims turnaround, appeals rate, patient satisfaction); run iterative tests and human-review loops; document outcomes and plan scale-up.

Across these considerations, be explicit about which workflows involve sensitive patient data and where natural language processing or machine learning components are used versus where agents orchestrate rule-based tools. That clarity helps teams design safe, effective systems that deliver measurable benefits—reducing staff administrative load, improving patient care, and unlocking operational value across the healthcare system.

AI agents can ingest new information, make evidence-based recommendations, carry out tasks, and coordinate with other agents and people as needed. In an industry facing skyrocketing costs and a shortage of workers, agentic AI can reduce the time staff spend on repetitive administrative tasks, speed up billing and claims workflows, and improve coordination among patients, clinicians, and payers. Start small with a focused 90–180 day pilot—such as automated scheduling or claims triage—measure outcomes (time saved, claims turnaround, patient experience), and scale successful pilots into multiagent workflows that deliver sustained benefits across the healthcare system.