Resolve tickets faster with agents that know your product
Support agents handle the same issues repeatedly. With Headkey, your agent builds a knowledge base of resolved tickets, spots recurring patterns, and maps how product components relate to known issues.
Three Primitives, One Cognitive Architecture
Each primitive serves a different purpose. Here's how they work for this use case.
Memories
Build a searchable knowledge base from resolved tickets
Every bug fix, workaround, and solution is remembered and retrievable by natural language. Your agent never forgets a resolution.
{
"content": "OAuth login fails with special characters in password. Fix: URL-encode before sending.",
"intent": "observation",
"tags": [
"auth",
"bug-fix"
],
"importance": "high"
}Beliefs
Detect recurring issue patterns
After seeing multiple auth failures, the agent forms the belief "the auth module has recurring special character issues" at 0.8 confidence. Patterns surface automatically.
{
"content": "The authentication module has recurring issues with special character handling.",
"intent": "decision",
"confidence": 0.8
}Relationships
Map issues to product components
The agent builds a graph connecting bugs to modules, customers to issues, and workarounds to root causes. Context comes in connected, not flat.
{
"content": "The OAuth login bug affects the Authentication Module.",
"intent": "relationship"
}Flat Memory vs. Structured Cognition
What changes when your agent has a mind, not just a vector store.
| Dimension | Flat Memory (RAG) | Headkey |
|---|---|---|
| Repeat issues | Searches from scratch each time | Instantly recalls prior resolutions |
| Pattern detection | Cannot detect trends across tickets | Forms beliefs about recurring problems |
| Product topology | No concept of component relationships | Maps bugs to modules, customers to issues |
| Knowledge sharing | Siloed per agent instance | Org-wide visibility for shared learning |
See It in Action
A customer support agent that builds a product knowledge graph, tracks issue patterns as beliefs, and reflects on belief health over time.
|Start building your support agent
Free to start. Plug-and-play cognition for any MCP-compatible agent in 60 seconds.