Agentic AI for Pets: The Future of Veterinary Care Is Context-Aware

If you’ve spent time inside human healthcare lately, you’ve probably heard the term: Agentic AI. Agentic AI refers to artificial intelligence systems that can independently analyze information, make decisions, and learn from experience, rather than just passively responding to commands. 

We’re seeing a shift from passive large language models toward intelligent systems that can reason, act, and remember — systems that don’t just respond to prompts, but collaborate with clinicians, sift through fragmented histories, and flag what might otherwise be missed. 

It’s already happening: 

Hippocratic AI is building safe, conversational healthcare agents designed to handle low-risk, high-volume medical tasks — think pre-op instructions or chronic disease follow-up 

Google’s Med-PaLM 2 is trained to reason across structured medical data and unstructured notes, scoring impressively high on medical licensing exams 

OpenAI and Penda Health recently piloted an AI clinical copilot that helps clinicians in under-resourced clinics in Kenya assess, triage, and guide care.

These aren’t just digital assistants. They’re specialized agents working in coordination — each one focused on a different aspect of care, connected through memory and orchestration. 

So here’s the obvious question:

Why don’t we offer the same to animals?

Veterinary medicine is decades behind. A recent survey found that over 60% of veterinary clinics still rely on paper records, making it difficult to track long-term health trends in pets. Your dog’s ALT has been rising for 18 months — but no one noticed, because the data was buried in a scanned PDF. Your cat had an abnormal thyroid value last year — but that chart lived in a different clinic’s system. Your horse’s electrolyte issues? Mentioned once during a phone consult, never written down. 

Unlike human care, where data is at least centralized (albeit clunky), veterinary records are: 

Dispersed across clinics, platforms, and formats 

Largely unstructured and inconsistent 

Missing longitudinal visibility — most systems show snapshots, not stories 

Even academic research confirms this gap. A 2023 study published in Frontiers in Veterinary Science noted that “veterinary data is highly heterogeneous, often incomplete, and rarely available in a standardized format suitable for modeling.” 1 Meanwhile, diagnostic AI research in veterinary medicine is still relatively narrow — mostly focused on radiology or specific disease classification, not systemic reasoning. 

At CompanAIn, we’re changing that.

We’re building agentic technology for animal care — AI that doesn’t just see the latest lab result, but knows the context behind it. Knows your pet’s breed and history. Knows what changed, and why it matters. Knows how to respond in a way that’s clear, relevant, and grounded in the full picture. 

We’re building technology that does more than respond — it remembers, interprets, and guides care over time: 

Lab results are translated using species — and breed-specific norms, paired with historical comparisons to highlight meaningful shifts 

Health data is automatically structured into a living timeline, giving pet owners and vets a longitudinal view of labs, notes, medications, and observations 

Subtle changes are proactively flagged — not just when a value exceeds a threshold, but when something looks off for your animal, based on past patterns 

Pet owners can log real-time observations and ask natural-language questions — with responses that reflect the pet’s full medical context, not just the most recent input 

USE CASE

Caper

Recurring inflammation

In early trials, CompanAIn helped a pet owner spot a pattern of recurring inflammation in their companion animal — a horse named Caper. Rather than attributing Caper’s discomfort to isolated incidents, the owner could see the bigger picture: connecting the dots between Caper’s history surfaced subtle but consistent signs tied to both his health and wellness. Acting on this insight, the owner adjusted Caper’s diet and replaced a once custom-fit saddle. At Caper’s next gait-analysis visit, the inflammation had resolved — an outcome that would have been missed without AI-powered longitudinal tracking. 

We’re not here to replace veterinarians. We’re here to amplify their insight and bridge the gap between visits — using AI to ensure no red flag, no pattern, no meaningful change is missed just because it was hiding in a PDF. 

Veterinary care deserves more than guesswork and gut checks. Pet owners deserve more than transactional visits and vague reports. Animals — who can’t speak for themselves — deserve a system that can. 

This is agentic technology, built for veterinary medicine. And at CompanAIn, we’re making it real.