AI Agents That Work Alongside Your Hospital Team
Most 'AI in healthcare' is a chatbot bolted onto a website. We built agents that analyze data overnight and prepare the morning brief, help your staff work 3x faster, and talk to patients on WhatsApp in their language. Three modes, one platform.
Three Agent Modes
Different jobs, different agents. Same AI Platform, same tools, same safety guarantees underneath.
Autonomous Agents
They work while you sleep
Backend supervisor agents that run without human interaction. Cron-driven and event-driven. Care gap analysis, action prioritization, data quality monitoring -- decisions made overnight, ready for your morning brief.
- Nightly care gap scans across entire patient population
- Prioritize and schedule follow-up actions automatically
- Data quality monitoring and anomaly detection
- Distributed locking and retry with backoff
- Cooldown periods to prevent patient message fatigue
Assisted Agents
Your staff, but 3x faster
CRM-embedded specialists that work alongside hospital staff. Reception, Doctor Lookup, Booking, Branch, Emergency -- each with dedicated tools. The agent assists; the human decides.
- Specialist agents with coordinator routing
- Generative UI: tool results rendered as React components
- Live edit mode on active patient records
- Multi-agent orchestration with context handoff
- Knowledge bank for live call support
Conversational Agents
Your patients talk to them directly
Patient-facing AI on WhatsApp and Voice. Appointment booking, lab results, medication refills. Safety guardrails enforced at every interaction -- these agents never diagnose.
- WhatsApp: 24-hour window compliance, session persistence
- Voice AI: real-time conversations in Hindi and English
- Appointment booking and care gap follow-up
- Never diagnoses, always escalates emergencies
- Complete audit trail of every patient interaction
CRM AI: Multi-Agent Orchestration
One coordinator, multiple specialists. Tools return React components, not just text. Agents operate on live records -- no copy-paste from a chat window.
Multi-Agent Orchestration
One coordinator agent receives all queries and routes to the right specialist. Each specialist has dedicated tools and system prompts. The patient doesn't know they are talking to five agents.
Generative UI Tools
Tools return React components, not just text. Appointment cards, patient summaries, care gap visualizations -- rendered live from tool results. The AI doesn't just answer; it shows.
Agent CMS
WYSIWYG agent builder. System prompt, tool selection, persona, behavior rules. Build a new specialist agent without writing code. We use it internally too.
Live Edit Mode
The agent operates directly on active records. No confirmation dialogs. It reads the record, identifies the action, executes. This is what makes it feel like a coworker, not a chatbot.
Tool Testing Sandbox
Test any tool in isolation -- validate inputs, check outputs, debug edge cases. Every tool is independently testable before you wire it into an agent. We learned this the hard way.
Skills Library
Diabetes care protocols, hypertension management, cardiac risk scoring. Agents load relevant clinical skills on-demand. Every response grounded in published clinical guidelines.
WhatsApp AI: Patient Self-Service
Patients book appointments, check lab results, and manage medications on WhatsApp. 24/7, in their language. Your front desk didn't have to be involved.
Capabilities
Safety Guardrails
Patient-facing AI without guardrails is a lawsuit waiting to happen. Every interaction is bounded, monitored, and auditable.
Backend Supervisor: Autonomous Execution
These agents analyze care gaps, prioritize follow-up actions, and flag data quality issues. No human in the loop. That is the point.
Execution Modes
Reliability Guarantees
Safety at Every Layer -- Not Just the Prompt
Topic Boundaries
Scope enforcement
Each agent has a defined scope. Ask it something outside that scope and it redirects or escalates. No creative improvisation.
Escalation Triggers
Hardcoded safety
Emergency keywords, sentiment spikes, clinical risk indicators -- all trigger immediate human escalation. Not configurable. Hardcoded.
Output Validation
Schema-enforced
Every LLM output validated against strict schemas before execution. Malformed or out-of-bounds responses get rejected and retried. The system does not trust the model blindly.
Audit Trails
Complete record
What was asked, what was proposed, what was approved, what executed, what resulted. Complete record. Every time.
How THB AI Prevents Hallucination
Typical Healthcare AI (Dangerous)
- ×LLM generates SQL directly from natural language
- ×No validation layer between LLM output and database
- ×Results look plausible but may be clinically wrong
- ×Hallucinated statistics presented as medical facts
- ×No audit trail for AI-generated recommendations
THB AI (Grounded)
- ✓LLM reasons over pre-computed, verified metrics
- ✓Strict schema validation on every state mutation
- ✓Planning docs provide clinical protocol grounding
- ✓Tool outputs are typed and testable independently
- ✓Complete audit trail: prompt, tools, actions, results
See AI Agents in Action
We will show you all three modes -- autonomous care gap closure, CRM-embedded assistants, and patient-facing WhatsApp AI. Real data, real safety, real outcomes.