Swarmcore

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Why we built SwarmCore

AI agents are becoming increasingly capable, but capability alone does not make a system autonomous. The harder problem is coordination: how multiple agents can divide work, verify each other's decisions, reason about uncertainty, and act as a collective system.

We started SwarmCore to explore this problem.

Our first practical experiment is a Bounty Hunter — an autonomous multi-agent workflow that continuously discovers AI and Web3 opportunities, evaluates their requirements, filters out opportunities that require spending or manual intervention, and produces a structured shortlist.

What began as a research experiment quickly became a practical test of autonomous coordination.

How it works

SwarmCore connects to live opportunity data and processes it through an agent-based pipeline:

Opportunity Discovery
        ↓
Eligibility Analysis
        ↓
Risk & Constraint Analysis
        ↓
Collective Reasoning
        ↓
Autonomous Opportunity Selection
        ↓
Report / Notification

The system is designed so that different agents can specialize in different parts of the problem instead of relying on one monolithic prompt.

For the hackathon, we are extending this architecture with NVIDIA Nemotron running through Nebius, using Nemotron for deeper reasoning and coordination between specialized agents.

What we learned

Building an autonomous system taught us that the difficult part is not simply calling an LLM.

The difficult part is designing reliable boundaries around autonomy.

An agent must understand:

• what it is allowed to do;
• what requires human approval;
• whether an action involves spending money;
• whether credentials or signing are required;
• how to handle incomplete or inconsistent data;
• and how to recover when an external API fails.

This led us to separate discovery, analysis, decision-making, and execution instead of giving a single agent unrestricted control.

Challenges

The biggest challenges were reliability and real-world data.

External APIs can change their response formats, listings can disappear, models can hit rate limits, and some opportunities contain requirements that cannot be safely inferred from a title alone.

We therefore added fallbacks, explicit autonomy policies, bounded model usage, and structured reports. SwarmCore currently avoids spending money, signing transactions, or submitting opportunities automatically without the appropriate safeguards.

Where we are going

The current Bounty Hunter is only the first experiment.

Our longer-term goal is to turn SwarmCore into a coordination layer for autonomous AI agents — a system where specialized agents can discover tasks, plan solutions, write and test code, verify results, and collaborate on execution.

The hackathon gives us an opportunity to test this architecture with NVIDIA Nemotron and Nebius and move SwarmCore from a research prototype toward a more capable autonomous agent system.

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for Swarmcore

Built With

• agent-orchestration
• ai-automation
• autonomous-agents
• generative-ai
• github-actions
• multi-agent-ai
• nebius-ai-cloud
• node.js
• nvidia-nemotron
• rest-api
• swarm-intelligence
• telegram
• typescript