KALKI AI employs a multi-agent framework built on two foundational standards:
MCP provides a secure barrier separating language model reasoning from underlying host tools, APIs, and file systems.
{
"jsonrpc": "2.0",
"id": "mcp-req-8842",
"method": "tools/call",
"params": {
"name": "kalki_vector_search",
"arguments": {
"query": "quantum encryption standards",
"top_k": 5,
"min_confidence": 0.82
}
}
}
{
"jsonrpc": "2.0",
"id": "mcp-req-8842",
"result": {
"content": [
{
"type": "text",
"text": "Extracted 3 relevant passages from enterprise security standard doc."
}
],
"isError": false
}
}
The A2A protocol governs asynchronous delegation between specialized agent personas.
graph TD
UserQuery["User Request"] --> SecurityAgent
SecurityAgent -->|Approved| PlannerAgent
PlannerAgent -->|Sub-task 1| ResearchAgent
PlannerAgent -->|Sub-task 2| MemoryAgent
PlannerAgent -->|Sub-task 3| ExecutorAgent
ResearchAgent --> ValidatorAgent
ExecutorAgent --> ValidatorAgent
ValidatorAgent -->|Validated Result| PlannerAgent
PlannerAgent --> Synthesis["Final Response Assembly"]
| Agent Persona | Role Description | Communication Channel |
|---|---|---|
| Planner Agent | Decomposes goals into Directed Acyclic Graphs (DAGs); manages state transitions. | A2A Bus (kalki.agents.planner) |
| Research Agent | Queries external web APIs, document stores, and RAG hybrid search engines. | A2A Bus (kalki.agents.research) |
| Memory Agent | Manages short-term, long-term, semantic, and episodic memory persistence. | A2A Bus (kalki.agents.memory) |
| Executor Agent | Runs MCP tools, API calls, and sandboxed code execution tasks. | A2A Bus (kalki.agents.executor) |
| Validator Agent | Runs hallucination checks, schema assertions, and factual groundings. | A2A Bus (kalki.agents.validator) |
| Security Agent | Monitors execution traces for prompt injection, privilege escalation, and unsafe commands. | A2A Bus (kalki.agents.security) |
{
"trace_id": "tr-908124a-771c",
"parent_agent": "PlannerAgent",
"target_agent": "ResearchAgent",
"message_type": "DELEGATE_SUBTASK",
"payload": {
"subtask_id": "st-001",
"instruction": "Fetch market trend data for AI chips from internal RAG database",
"required_context": {
"filters": { "year": 2026 }
},
"timeout_ms": 3000
},
"timestamp_utc": "2026-07-21T20:39:00Z"
}