01What does Karmaflow mean by "the review layer"?+
The reflective pass every Karmaflow agent runs after the live conversation ends. Same agent, second function. It reads the full transcript as evidence and acts on what only a complete record can reveal — writing a structured memory record, completing the handoffs the live work didn't finish, sending a follow-up only when one is earned, and filing a complete AI agent audit trail. Non-conversational by design. The member never sees it; they only see the better next conversation.
02How is the review layer different from AI agent observability tools like LangSmith, Braintrust, or Arize?+
Those platforms sit outside the agent. They watch traces and metrics, surface drift to engineers, and tell you when something looks wrong — useful, separate problem. The review layer sits inside the agent itself. It reads what the agent just did as evidence and then takes action: memory writes, completed escalations, customer follow-ups, audit rows that include live-agent competency flags. Observability tells your engineers something is off. The review layer fixes the customer's missed handoff before they notice.
03How is this different from contact-centre QA software like Observe.AI, Zendesk AutoQA, or Balto?+
Contact-centre QA tools score agents — human or AI — against a rubric after the call so a QA team can coach with the result. They are scorecards. The review layer is operational: it doesn't just grade the conversation, it completes it. Escalations are finished, memory is written, follow-ups are sent, and the same pass writes the score onto the audit row so architects above the loop can tune the live prompt directly. One agent, both jobs — no parallel platform to integrate.
04Does the review layer help with the EU AI Act, Colorado AI Act, and other 2026 regulations?+
Yes, on the parts the regulation actually cares about. Every audit row captures who initiated the action, what data was retrieved, which policy was in force at the time, and which model and configuration produced the answer — the four pillars a defensible AI agent audit trail needs. The EU AI Act's high-risk obligations are enforceable from August 2, 2026, with penalties up to €35M or 7% of global turnover; the Colorado AI Act is enforceable from June 2026. Your compliance team gets the evidence trail, not a PDF policy.
05Does the review layer prevent AI hallucinations?+
It is post-hoc — it reads the transcript, it does not pre-empt it. Live hallucination prevention is the live agent's job (structured outputs, retrieval grounding, the rules domain experts write into the prompt). What the review layer does is catch a hallucination after the fact, flag it on the audit row with severity and a suggested tune, and feed that flag back into the prompt the next live conversation will run on. Guardrails block; the review layer learns. That is what closes the quality loop.
06Will customers feel watched by a second AI?+
They never see it. The review layer is non-conversational by design — its only outputs are internal memory, completed routing, an occasional follow-up email if one is earned, and an audit row. When the member does receive a follow-up, the test the layer is held to is: would I be genuinely pleased to receive this in my inbox? The voice is “thoughtful friend who works at CAA,” not marketing bot. And six exclusion gates make sure the layer stays silent in the cases where silence is the service.
07Can our compliance and audit teams replay every decision the agent made?+
Yes. Every session ends with an audit write that captures the path the review pass took, why a follow-up did or did not fire, which knowledge-base sources and external domains informed the enrichment, which custom schema fields were added, and any live-agent competency breaches. Exportable to your SIEM. Versioned against the rules in force at the time. This is the “tamper-evident, replayable” standard the 2026 enterprise-AI governance literature now expects.
08Is the review layer a separate product? A second agent? A second bill?+
No, no, and no. The review layer is a function of every Karmaflow agent — voice, chat, SMS, email — included in the same deployment, the same platform, the same bill. Domain experts in your organisation write the discipline (tone, contact surfaces, exclusion gates, pricing claims) in plain English; we compile it into runtime guardrails the layer honours at every step. The live agent runs the conversation; the same agent runs the reflective pass afterwards. Singular agent, plural function.