Amigo Deep Dive: Healthcare AI Guy

The following is a direct transcription of the interview. To read this post on Healthcare AI Guy's website, please visit this link.

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Company Deep Dive: Amigo

Perspectives from the people building the future of health AI…

We sat down with Ali Khokhar, Co-Founder and CEO of Amigo, a platform for building safe, reliable AI agents in healthcare. Rather than building a one-size-fits-all "AI doctor," Amigo is focused on providing the infrastructure and tooling to help healthcare organizations create their own highly customized agents, from virtual clinicians to care coordinators, with safety, transparency, and control built in.

Ali shared how the team is approaching trust, why they see growing demand for patient-facing AI, and how the company is planning for a future where AI agents become trusted, tested pillars of the care delivery ecosystem.

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Let’s start from the top. What is Amigo and what problem are you solving?
Amigo is a platform for deploying AI agents in high-risk industries, with healthcare as our primary focus. Everyone is talking about agents, but in healthcare, you can’t afford failures. Our goal is to make these agents trustworthy enough to operate in environments where the cost of error is very high. That means providing organizations with the infrastructure to build, train, test, and monitor agents they can trust, starting with clinical use cases.

We don’t provide an off-the-shelf “AI doctor." Instead, we help companies build their own clinical agents, tailored to their needs. For example, an AI clinician built for an urgent care provider looks very different from one built for a women’s health clinic. Amigo is the infrastructure that makes that customization and control possible.

How do you define trust when it comes to AI agents in healthcare?
To us, trust means confidence that an agent will behave reliably in the way you want it to. That confidence depends on three things: control, alignment, and observability.

Control means being able to shape and constrain the behavior of the agent as the clinical expert. Alignment is about making sure the agent acts according to evolving expectations and regulatory needs. And observability means you can monitor and understand the agent's decisions in real time.

When these three are in place, we believe you're in a position to trust an agent.

What does that look like in practice when someone builds an agent using Amigo?
We guide each of our partners through a structured process. We start by working with a clinical expert and a product lead to define the agent's "operable neighborhood"—the set of scenarios where it can safely operate. Then we simulate that environment and stress-test the agent using synthetic patient interactions, millions of times. This allows us to ensure reliability and performance in an environment that most accurately reflects that specific partner’s real patient population.

After deployment, we keep that simulation loop running. When an agent performs poorly or encounters something new, we generate more simulated scenarios and retrain it. It’s like Waymo: you don’t deploy to every city at once. You start in neighborhoods where you have confidence, then expand into new ones—safely.

How do you ensure clinical teams are comfortable adopting AI agents?
It all starts with involvement. The clinicians are co-creators. When we work with a healthcare organization, we pair one of our agent engineers with a clinician and a product lead from their team. That trio defines how the agent should behave, where it should operate, and what success looks like.

By keeping the clinical voice in the loop from day one, we build trust and accountability. When it comes to successful adoption, cultural readiness is just as important as technical performance. And when clinicians help train and test the system, they’re much more confident putting it in front of their patients. And by testing the agent via millions of simulated conversations, clinicians gain the confidence that it will perform really well before it enters the real world.

What are some of the most common use cases you’re seeing?
A lot of our partners are building agents to help overworked care teams. Think: 24/7 support for follow-up questions, intake and triage, lab result debriefs, medication guidance, or just general care navigation.

We also support fully conversational agents that can perform actions, not just offer advice. Through our Amigo Actions tooling, agents can order labs, write to the EMR, or message other members of the care team. This helps our partners build complex clinical workflows that save their clinicians a lot of time.

What makes Amigo different from traditional "build or buy" approaches?
We offer something in between. You’re not buying a rigid, off-the-shelf agent. But you’re also not hiring a team of ML engineers to build everything from scratch. Instead, you get the tooling, memory system, reasoning engine, and simulation framework to build your own agent, with full control, fast iteration, and measurable performance.

We think the right way is to build at the right layer. You focus on determining the agent’s medical reasoning and clinical behavior; we handle the orchestration, infrastructure, and safety tooling. Healthcare organizations are experts at providing high-quality care, not at building complex AI architecture.

What’s under the hood? Are you training your own models?
We don’t train our own models. Frontier AI labs have already invested billions of dollars into training their foundation models and they already contain all the specialized knowledge and medical reasoning capabilities needed. There’s a whole body of academic research that shows this. The real gaps that need to be bridged are control and trust, and our approach focuses on correctly activating this knowledge and reasoning.

To do this, we built a cognitive architecture that sits on top of the foundation models, with custom systems for memory, reasoning, and behavior adaptation. We orchestrate multiple models in real time depending on the task. One model might be used for clinical reasoning, another for empathetic response generation, and another for knowledge retrieval. This lets us route to the best tool for the job. The result is better performance, more control, and lower latency.

What metrics matter most when evaluating AI agents for healthcare?
There’s no one-size-fits-all benchmark. We work with each of our partners to define a "success scorecard" based on their clinical workflows. That includes both safety metrics like accuracy, clarity, and handoff reliability, and experience metrics like tone, empathy, and response time.

When we iterate on an agent, we don’t stop until it matches or exceeds human performance in that setting. Our internal simulator agents are adversarially testing and actively try to surface edge cases—for this reason, one of our recent partners saw their agent perform even better in the real world than in simulations.

What’s your long-term vision?
We believe we're moving from a human-based economy to an agent-based one. But to get there, we need infrastructure that verifies performance, ensures safety, and builds trust. In healthcare, that means AI clinicians need to be "credentialed" in the same way human physicians are. That’s what we’re building toward: the verification layer for the agent economy.

Any final thoughts?
Much of the industry is still focused on back-office automation and scribes. We think the bigger opportunity is in frontline patient-facing care, and we’re already seeing it work. The biggest blocker isn’t the tech. It’s the assumption that there’s no safe way to do this.

There is. And the opportunity for impact is tremendous.

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Healthcare AI Guy Summary

What stood out, what’s tricky, and why it matters…

Amigo is building the infrastructure for safe, scalable AI agents in healthcare. Instead of launching a one-size-fits-all “AI doctor,” they’re helping health orgs build their own custom agents, from virtual clinicians to care coordinators, trained and tested to perform safely in high-stakes, patient-facing roles.

Each agent gets simulated before it goes live. Clinical teams define what “good” looks like, Amigo builds a synthetic environment to match it, and the agent gets trained and tested there until it hits the mark. That same loop keeps running post-deployment. When new edge cases show up, the agent retrains and improves automatically.

It’s a different take on AI enablement. While most of the market is still focused on scribes and billing tools, Amigo is betting that the real value lies in front-office care (triage, navigation, follow-ups) and that organizations want control, not pre-built bots. We’re excited about this bold vision, and with a $6.5M seed round co-led by General Catalyst and GSV Ventures, Amigo’s already shifting that future into gear.

What stood out

• Agents get real-world reps before going live: Before an agent talks to a single patient, it’s tested across thousands of simulated conversations modeled after real-world cases. These aren’t generic benchmarks. They’re created in collaboration with each customer’s clinicians.
• It’s not advice-only: With Amigo Actions, agents can now do more than talk. They can order labs, write to the EMR, and route issues to care teams when permissions allow. That’s a meaningful step beyond most “chatbot” systems.
• Retraining happens on the fly: If an agent performs poorly or gets pushed out of scope, Amigo automatically generates new training scenarios and loops them into the simulation. The system improves itself, without waiting for manual red-teaming.
• Build meets buy: Health orgs don’t just install an agent and hope it works. They define how it behaves, what its boundaries are, and when to escalate. Amigo provides the infrastructure and orchestration to make that kind of control feasible.
• They’re serious about verification: The long-term vision isn’t just deploying agents—it’s credentialing them. Think: infrastructure that verifies, tracks, and validates agent performance like a digital version of board certification.

What’s tricky

• Frontline care raises the stakes: Back-office AI can be error-tolerant. Patient-facing agents can't. The margin for error is smaller, and the bar for safety, transparency, and oversight is much higher.
• Adoption still hinges on trust: Amigo embeds clinicians in the training process, but even then, it takes time to change mindsets. Clinicians need to feel like co-owners, not just testers.
• Customization adds pressure: Because every agent is different, the success of each deployment depends on how well a customer executes. That’s a strength, but also a risk, especially if orgs underestimate the lift.

Final thoughts

Amigo is going after one of the hardest but highest-leverage problems in healthcare AI: building agents that actually deliver care. Their infrastructure-first approach lets customers move faster without cutting corners. If they can keep proving that agents can be safe, transparent, and controllable, they won’t just help the market adopt AI; they might help redefine what trustworthy AI in healthcare looks like.