How it works
What actually happens inside a turn — retrieval, memory, tools, context, channels, and what stays private.
Grounded answers
How a document becomes something an agent can answer from — chunking, the context header, query rewriting, and the relevance floor.
Per-customer memory
How conversations become durable facts about a customer — derivation, deduplication, importance, and what gets recalled.
Tools and MCP
How an agent goes from answering to acting — the tool loop, context budgets, and connecting your own systems over MCP.
Why agents get worse as context grows
Context rot, in concrete terms. Every turn is one self-contained request to the model; a long or tool-heavy one measurably lowers the accuracy of the answer. What that request contains, what gets dropped when it does not fit, and why the platform re-renders it before writing the answer instead of summarising tool results.
Where a conversation lives between turns
If every turn is one self-contained request, where does the rest of the conversation sit? What gets stored per turn and what never does, the verbatim window, how older turns are folded into a rolling summary exactly once, what happens when a single turn still overflows, and which facts outlive the session.
One agent, every channel
How the web widget, Telegram and WhatsApp reach the same agent with the same memory — identity resolution, the shared control flow, and what stays channel-specific.
Scheduled follow-ups
How an agent schedules work for later and delivers the result back to the channel it came from — durable tasks, atomic claiming, and recovery.
Private by design
Isolation enforced in the database, field-level encryption with per-user keys, and an honest account of what the end-to-end layer does and does not give you.
Running on open models
How the model layer stays separate from the agent layer — capability probing, multi-backend routing, and the four ways small open models break on tool calls that the gateway handles for you.