By Heli and Marko Helskyaho

Last but definitely not least:

Reindeer Health Copilot: Santa’s AI Vet on Duty 24/7.

Reindeer Health Copilot is a proactive wellness guardian or a “digital veterinarian” for Santa’s nine famous reindeer, ensuring they’re flight-ready for Christmas Eve. It monitors real-time vitals, predicts fatigue or issues, and recommends fixes to keep the team flight-ready for the big night. 

What this Agent does:

1. Ingests real-time telemetry: Pulls data from sensors (heart rate, steps, “carrot intake” as energy proxy, flight altitude stress, rest hours) stored as time-series JSON in tables like REINDEER_TELEMETRY.
2. Analyzes trends: Uses hybrid search across:
   • Relational metrics for current stats.
   • Vector embeddings of historical health logs to spot patterns (for example, “Prancer’s heart rate spikes in cold winds”).
   • Graph connections to team dynamics (for example, “Dancer’s pace affects Cupid’s endurance”).
3. Reasons Proactively: Follows the ReAct loop:
   • Runs NL2SQL for quick aggregates (for example, “Average carrot consumption last 7 days”).
   • Calls weather REST APIs for environmental risks (for example, icing on antlers).
   • Uses LLM reasoning to score flight readiness and simulate scenarios (for example, “What if we hit turbulence over the Atlantic?”).
4. Acts and alerts: Recommends or automates actions. On Christmas Eve (today!), it runs continuous checks for last-minute go/no-go decisions.

Built with Select AI Agents’ ReAct (Reasoning + Acting) pattern, the Copilot follows a loop:

Observe (gather data), Reason (analyze with an LLM), Act (execute tools), and Reflect (learn from outcomes). Everything runs natively in Autonomous Database 26ai, inheriting enterprise security like row-level access (e.g., only elves with clearance see Blitzen’s vitals). Key components of the solutions are:

• Data Sources:
  • Time-series JSON logs (REINDEER_TELEMETRY table with fields like reindeer_id, timestamp, carrot_intake, heart_rate, flight_hours).
• Tools Used:
  • NL2SQL: Converts natural questions like “Is Vixen overworked?” into optimized queries.
• • RAG (Retrieval-Augmented Generation): Pulls context from vectorized health guidelines or past incident reports.
• • LLM Reasoning: Uses OCI GenAI or a local model (for example, Llama via Ollama) to score risks and simulate scenarios.
  • External Integration: Optional REST calls to weather APIs for flight risk assessment.
• Memory: Maintains short-term (current flight) and long-term (seasonal trends) context for multi-turn interactions.

This could be a sample report from the agent:

Korvatunturi Health Bulletin – Christmas Eve 2025  

Scanning telemetry for Dasher, Dancer, Prancer, Vixen, Comet, Cupid, Donner, Blitzen, and Rudolph…  

Overall Fleet Status: GREEN – 98.4% readiness  

• Rudolph: Nose glow at 100% (extra bright for fog over London). Carrot intake optimal.  

• Dasher & Dancer: Top speed calibrated; minor wind chill noted—recommend heated blankets pre-takeoff.  

• Prancer & Vixen: Energy reserves high after double oats; no issues. 

• Comet & Cupid: Slight fatigue trend from rehearsals (+8% heart rate)—suggest 30-min rest cycle now.  

• Donner & Blitzen: Thunder-ready; weather API shows clear skies ahead.  

Predicted Risks: 3% chance of mid-Pacific yawn spike, mitigated by in-flight snack protocol.  

All systems ho-ho-go! Sleigh cleared for departure at 20:00 UTC.  

Merry Christmas to all, and to all a safe flight! 🦌✨

(Sources: REINDEER_TELEMETRY_VECTORS, WEATHER_API, FLIGHT_HISTORY)

Why it’s technically impressive (and Reindeer-approved)

• Predictive power: Spots issues hours ahead using time-series trends and vector similarity to past Christmases.
• Secure as the Korvatunturi Vault: All vitals stay in the database. Row-level security prevents unauthorized elf (or hacker) peeks at Rudolph’s glow metrics.
• Scales to Christmas crunch: Handles billions of telemetry points efficiently even during tonight’s peak.
• Real-world twin: Swap reindeer for delivery trucks, wind turbines, or hospital monitors and the same agent framework keeps operations humming.

The Reindeer Health Copilot is the agent making sure everything runs smoothly tonight… because even Santa needs a copilot. 🎅🦌

Ho ho healthy holidays! How were all these components created? Here’s some code examples.

To create the ReindeerHealthCopilot agent:

BEGIN

  DBMS_CLOUD_AI_AGENT.CREATE_AGENT(

    agent_name => ‘ReindeerHealthCopilot’,

    attributes => ‘{

       “profile_name”: ‘OCI_GPT4O’,

                       “role”: “You are a jolly reindeer health expert. Analyze data, predict issues, and suggest fixes with festive optimism.”

     }’,

     description    => ‘Monitors reindeer vitals for Christmas readiness’

  );

END;

/  

Add some Tools:

BEGIN

  DBMS_CLOUD_AI_AGENT.CREATE_TOOL(

    tool_name  => ‘WeatherForecast’,

    attributes => ‘”instruction”: “This tool fetches and returns the weather forecast from the specified URL.”,

      “function”: “get_url_content”

‘

  );

END;

/

: more tools

: more tools

Create some Tasks:

BEGIN

  DBMS_CLOUD_AI_AGENT.CREATE_TASK(

    task_name => ‘ReindeerHealthTask’,

    attributes => ‘{“instruction”: “Handle reindeers: {query}”,

                    “tools”: [“REINDEER_HEALTH_VECTORS”, “WeatherForecast”]}’

  );

END;

/

: more tasks

: more tasks

Create the Team:

BEGIN                                                               

  DBMS_CLOUD_AI_AGENT.CREATE_TEAM( 

    team_name  => ‘SantasAgents’,                                                           

    attributes => ‘{“agents”: 

[{“name”:”NiceList Analyst”,”task” : “NiceListTask”},

 {“name”:”WishList Resolver”,”task” : “WishlistTask”}

 {“name”:”SleighRoute Optimizer”,”task” : “SleighRouteTask”}

 {“name”:”Giftwrap Foreman”,”task” : “GiftwrapTask”}

 {“name”:”ReindeerHealth Copilot”,”task” : “ReindeerHealthTask”}],

                    “process”: “sequential”}’

);                                                               

END;                                                                     

/

And finally an example of calling the Agents:

l_response :=  DBMS_CLOUD_AI_AGENT.RUN_TEAM(

  team_name   => ‘SantasAgents’,

  user_prompt => ‘Check Comet”s energy levels after last rehearsal. Flight tomorrow?’

 ,params      => ‘{“conversation_id”: “‘ || l_conversation_id || ‘”}’

);

Why Santa (and every CIO) loves Select AI Agents:

• Zero data leaves Korvatunturi (or your data center) 
• Agents respect row-level security (for example, Mrs. Claus still can’t see the Naughty list) 
• Quick response even with billions of wish-list rows 
• Full audit trail for when the EU asks why little Pierre got coal 
• Scales from one laptop demo to planetary delivery night without code changes

With Select AI Agents, your database doesn’t just store data. The database thinks, reasons, and acts like the world’s jolliest autonomous workforce. 

Merry Christmas! 🤶🧑‍🎄🌲🎁 🦌