Wildreason
Wildreason provides a platform for building agent organizations—teams of AI agents that collaborate autonomously on complex, multi-stage work. Agents split tasks, hand off work, and review each other's output, operating for days without human intervention while recording every action on an auditable chain. The system has been used to build WhaleGuard, an autonomous voyage-planning system that saved an estimated $107,271 on a single New York-Rotterdam crossing.
- AI Agents
- Artificial Intelligence
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Most AI agents today operate one prompt at a time, requiring a person to approve each step, which limits their usefulness for complex, multi-stage work. This creates a bottleneck where human oversight is needed constantly, preventing agents from carrying out long-horizon tasks independently and slowing down the pace of work.
Solution
Wildreason provides a platform for building agent organizations—teams of AI agents that work together autonomously toward a defined goal. Agents split work, hand it off to each other, check one another's output, and reason together in real time, drawing on the right model for each task. The system can operate for days without human intervention, with every action recorded on an auditable chain. Wildreason gives agents structure through roles, versioned skills and memory, and team governance, enabling small teams to accomplish work that once required an entire company.
Target Audience
Wildreason serves a small group of businesses and labs each year that need autonomous AI teams to carry out complex, multi-step operational work, such as voyage planning, game development, and research tasks.
Features
- Multi-agent orchestration with task splitting, handoffs, and peer review for long-horizon work
- Freeform natural-language communication between agents rather than rigid structured calls
- Versioned agent identity, skills, and memory, with all actions written to an auditable chain
- Team governance through shared norms, ownership, and peer accountability rather than hardcoded rules
- Agents can access external tools and data sources, including MCP servers and web resources
- Interface design that surfaces only the decisions waiting on a person, minimizing required human input
- Agents can run experiments and gather real-time information rather than relying only on training data