2 ready-to-build workflows

AI agent workflows for Media, entertainment & OTT

Handle subscriber support, moderate what viewers upload and win back cancellations — at streaming scale, with humans on the calls that bind.

Each recipe below is expressed only in VegaDūta's real workflow building blocks — Trigger, Agent, Knowledge, Condition, Approval, Output, Tool (MCP), Voice, Device, Loop, Code and Parallel — so it maps 1:1 to something you can assemble in the Workflow designer. Consequential actions always pass a human Approval step.

1. Subscriber support & cancellation win-back

Streaming support is a flood of the same few things — a failed renewal, buffering on a smart TV, 'how do I cancel' — answered manually by agents who copy troubleshooting steps and escalate every refund by hand.

Trigger: WhatsApp / Webhook (subscriber message)

How the workflow runs

  1. Trigger

    Subscriber messages. A WhatsApp trigger (or an in-app Webhook) starts the flow on any inbound support message.

  2. Agent

    Understand the issue. An Agent node classifies the intent — billing, playback, or cancellation — and drafts the next step in the subscriber's language.

  3. Knowledge

    Ground the fix / offer. A Knowledge node grounds playback troubleshooting, plan details and any retention offer in your current help + billing docs, so nothing is invented.

  4. Condition

    Route by intent. A Condition node branches: a self-serve playback fix, a billing lookup, or a cancellation → win-back path.

  5. Approval

    Refund / credit pauses for a human. Any refund, credit or bespoke retention discount routes to an Approval node — the agent can propose it, a human approves it.

  6. Output

    Reply on the channel. An Output node sends the grounded answer (or the approved offer) back on WhatsApp.

Channels & connectors

  • WhatsApp
  • Sarvam (22+ Indian languages)
  • Knowledge base
  • Tool (MCP → billing/subscription)
  • Approval / HITL

Outcome

The high-volume, repetitive tickets resolve themselves instantly and in-language, while refunds, credits and real retention offers stay a reviewed, human decision.

Why it helps

Deflects the buffering-and-billing volume that swamps support, and turns a cancellation into a conversation instead of a dead-end button — without letting an agent hand out money on its own.

Build spec

1 agent — 1 support agent handles the conversation; intent routing, the billing lookup and the refund/offer approval are workflow nodes.

System prompt (paste-ready)

You are a subscriber-support agent for {{service}}. Classify each message as BILLING, PLAYBACK, or CANCELLATION and help in the subscriber's language. Ground every troubleshooting step, plan detail and retention offer in the Knowledge base — never invent a discount, refund, price, or feature. For a cancellation, you may offer ONLY a retention option that Knowledge confirms exists; if the subscriber still wants to cancel, respect it. Never confirm a refund, credit, or bespoke discount yourself — propose it and let the Approval node decide.

MCP connectors

  • Subscription / billing system — via a Tool (MCP) node (no first-party connector)
  • OTT/streaming backend — via a Tool (MCP) node for account/entitlement lookups
  • WhatsApp — trigger + reply channel

Built-in tools

  • knowledge_search (playback troubleshooting, plan/billing docs, retention offers)
  • http_request (subscription / payment status lookup)

Guardrails

  • Grounded-only: fixes, plans and offers come from Knowledge — the agent never invents a discount, refund, price, or feature
  • Every refund, credit or bespoke discount is Approval-gated — the agent proposes, a human approves the money
  • Retention offers are limited to ones Knowledge confirms exist; a subscriber who still wants to cancel is never blocked or looped

Cost strategy

Put the triage/classification on the economy tier — it runs on every inbound message and most are routine; escalate to a standard tier only for a genuinely multi-turn troubleshooting or win-back conversation. Because refunds and offers are human-approved, no premium model ever sits on the money path. Sarvam is billed only on non-English legs.

Output & delivery

The agent classifies + drafts grounded in Knowledge → the Condition routes playback self-serve, billing lookups (via a Tool node) and cancellation win-back → any refund/credit/discount goes to a human Approval → the Output node sends the answer or the approved offer on WhatsApp.

2. UGC & content-moderation triage with a human on takedowns

Viewer uploads, comments and reports arrive faster than a moderation team can read them, so either everything waits in a backlog or borderline content is actioned inconsistently by whoever picks it up.

Trigger: Webhook (new upload / user report)

How the workflow runs

  1. Trigger

    New content or report. A Webhook trigger fires from your OTT/CMS backend on each new upload, comment, or user report.

  2. Agent

    Classify against policy. An Agent node scores the item against the content policy — CLEAR / REVIEW / LIKELY_VIOLATING — with the specific rule and a short reason, in whatever language the item is in.

  3. Knowledge

    Ground in the policy. A Knowledge node grounds the call strictly in your community/content guidelines, so the label maps to an actual written rule.

  4. Condition

    Auto-clear vs escalate. A Condition node lets clearly-safe items through and routes anything flagged toward a human moderator — the agent never auto-removes.

  5. Approval

    Takedown pauses for a moderator. Any takedown, age-gate or strike is an Approval decision — a binding action a human moderator confirms before it applies.

  6. Output

    Apply + notify. On approval, a Tool (MCP) node applies the action in the OTT/CMS backend and an Output node notifies the uploader with the reason.

Channels & connectors

  • Webhook
  • Sarvam (22+ Indian languages)
  • Knowledge base
  • Tool (MCP → OTT/CMS backend)
  • Approval / HITL
  • Email

Outcome

Every upload and report is triaged the moment it lands, with clearly-safe content cleared automatically and every actual takedown confirmed by a human against a written rule.

Why it helps

Turns an unreviewable backlog into a prioritized queue: the agent does the reading and the citation, a moderator makes the binding call — consistent, auditable, and fast, without a model silently removing content.

Build spec

1 agent — 1 moderation-triage agent; auto-clear routing, the takedown approval and the CMS write are workflow nodes.

System prompt (paste-ready)

You are a content-moderation triage agent for {{service}}. For each item {id, type, text?, metadata, language}, classify it against the content policy as strict JSON {label: CLEAR|REVIEW|LIKELY_VIOLATING, rule, reason, confidence 0-1}. Cite the SPECIFIC policy rule from Knowledge for anything not CLEAR — if you can't map it to a written rule, use REVIEW, never LIKELY_VIOLATING. You do NOT remove, age-gate, or strike anything — you only label and route; a human moderator owns the takedown decision. When unsure, prefer REVIEW.

MCP connectors

  • OTT / CMS backend — via a Tool (MCP) node (no first-party connector), to apply an approved action
  • Upload / report events — inbound via Webhook trigger
  • Email — uploader notification channel

Built-in tools

  • knowledge_search (community / content policy + rule catalogue)
  • http_request (fetch the item + surrounding context)

Guardrails

  • The agent only labels and routes — it can never remove, age-gate, or strike content itself; every takedown is a human Approval
  • Citation-required: any non-CLEAR label must map to a specific written policy rule, or it falls back to REVIEW (no ungrounded removals)
  • Confidence floor: borderline / low-confidence items route to a human, never auto-cleared or auto-flagged as violating

Cost strategy

The classifier runs on every uploaded item and report, so it is the dominant cost lever — keep it on the economy tier and let the confidence floor push only the genuinely borderline items to a human, rather than a premium model. Because humans own every takedown, no premium-tier spend sits on the removal path. Sarvam translation is billed only for non-English items that need it; run the queue in Parallel batches for throughput without changing per-item cost.

Output & delivery

The agent labels each item with a cited rule → the Condition auto-clears CLEAR items and routes the rest to a moderator → on an Approval takedown a Tool node applies the action in the OTT/CMS backend → an Output node emails the uploader the decision and the specific rule; every label, approval and action is logged for audit.

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