AI Agents That Know When They Don't Know
Coordination infrastructure for autonomous agents.
Field-based communication. Consistency checking.
Pattern learning. Built for the agentic future.
The Problem with AI Agents Today
🎣
Hallucinate
Make up facts with complete confidence
🏝️
Isolated
Can't coordinate without orchestration
🔄
Stateless
Forget everything between requests
How Reflexia Fixes This
✋
Abstention
Agents say "I don't know" when unsure
🌊
Field Coord
Agents sense each other automatically
🧠
Patterns
Learn from experience across time
How It Works
Five simple steps to coordinate your AI agents
1
Register Your Agents
Each agent gets a position in the shared field
2
Sense Before Acting
Agents check the field to know if they should activate
3
Modify After Acting
Actions ripple through the field, informing others
4
Check Consistency
Multiple reasoning paths → confident answers
5
Learn Patterns
Store what works, retrieve it later
Get Started in Minutes
Simple API, powerful coordination
Python Example
from reflexia import ReflexiaClient
client = ReflexiaClient(api_key="rk_...")
# Register agent
agent = client.register_agent("classifier-1", "classifier")
# Sense field before acting
field = agent.sense()
if field.should_activate:
# Run multiple trajectories
trajectories = [llm.classify(data) for _ in range(3)]
# Check consistency
result = client.check_consistency(trajectories)
if not result.abstained:
# Confident answer
agent.modify(delta=0.1, outcome="success")Simple, Usage-Based Pricing
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3 agents
- Basic features
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