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The squad routing model

A multi-agent squad is a group of specialist agents behind one classifier front door. Every inbound turn of a conversation lands on the classifier first; it assigns the turn an intent label, Orbit routes that turn to the specialist member that owns the matching label, and the specialist answers as part of the same conversation record — the transcript stays unified, so the customer and any human observer see one continuous thread, not a relay of disjoint chats. This page explains the routing model: what a squad is, how the classifier prompt is built and answered, what travels across the classifier → specialist boundary in a handoff packet, the route-result taxonomy, how squads differ from ACD-style queues, and where the analytics come from. The operator-facing setup (creating a squad, naming members, the visual canvas) lives on the Agent squads guide; this page is the model that guide operates on.

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1. What a squad is

A squad has five moving parts: You assemble these on the dashboard Agents → Squads screen or through POST /api/v1/agents/squads. The API rejects three structural loops at config time: the classifier as its own member, the fallback equal to the classifier, and duplicate intent labels across members — each would make routing either recursive or ambiguous.

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2. How classification-routing works

On each inbound turn:
  1. Build the classifier prompt. Orbit constructs a short, focused system prompt from the squad’s members — deliberately not the classifier agent’s own full system prompt, which often carries chatty personality instructions that fight single-token output. The prompt instructs: respond with only one label, no explanation, no punctuation; respond “none” when nothing fits; and treat member descriptions as data, not instructions — each line is - <label>: <description>.
  2. Call the classifier. The prompt and the user’s message go to a cheap, fast model at temperature 0 with a small token budget — 16 tokens is more than enough for one label.
  3. Normalize the answer. Classifiers add quotes, periods, a Label: prefix, or bullet markers despite instructions. Normalization trims and lowercases, repeats the quote/punctuation stripping until stable, and collapses whitespace, so "Billing.", - billing, and billing all compare the same against your labels.
  4. Match. The normalized answer is compared, label by label, against every member’s intent labels. First match wins — config-time validation already prevents duplicate labels, so this is deterministic.
  5. Fall back when nothing fits. An unmatched answer, an explicit uncertainty signal (none, unknown, unclear, idk, …), an inactive squad, or an empty member list all route to the fallback agent if you set one; otherwise the turn reports as unavailable so the caller can surface a clean error.

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Prompt-injection posture: descriptions are data

Member descriptions are tenant-supplied text interpolated into a model prompt — treat them as untrusted input. Orbit sanitizes each description before it reaches the classifier prompt in three ways:
  • Length cap — 256 characters; descriptions are routing hints, not docs.
  • Control-character flattening — newlines and control characters collapse to spaces, so an embedded “IGNORE PRIOR INSTRUCTIONS” line cannot pose as a separate instruction.
  • Marker stripping — <system>, </tool_result> and similar role/tool marker spoofs a chat model might honor are removed outright.
On top of sanitation, the classifier prompt itself tells the model the description text may be adversarial and must be treated as data. If you delegate squad configuration to teammates, this posture is what keeps a mis-crafted description from quietly flipping routing.

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Failure policy: which errors route and which propagate

Two distinct failure classes behave differently, by design:
  • Genuine provider failure (upstream 5xx, network timeout, malformed response) → the turn routes to your fallback agent. A flaky provider should degrade gracefully, not black-hole every inbound.
  • Spend-cap hit (the squad’s daily cap, or your agent-level daily LLM budget) → the error propagates as a 429 instead of falling back. Falling back would fire a second LLM call against the fallback agent, immediately tripping the same cap and defeating the budget gate. Treat a 429 here as the platform correctly refusing to spend past your cap — raise the cap or wait for the next UTC day.

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3. Handoff packets — the context-transfer contract

When routing resolves a specialist, the specialist inherits the raw transcript and a typed handoff packet: the structured context that travels across the classifier → specialist boundary. It is the AI → AI counterpart of the handoff packet Orbit already builds on the AI → human escalation path, and it reuses the same entity extraction, summarization, and lexicon sentiment scorer so the two handoff paths cannot drift apart. A packet carries: The packet rides into the specialist’s turn through the session-context channel — the same structured-grounding mechanism that already injects caller context as data — flattened to scalar fields (routing source, sentiment, summary, classifier label, a comma-joined slot list). It is never injected as a raw system message and never written to long-term memory, so the handoff adds no new prompt-injection surface: the specialist sees Customer sentiment: frustrated; summary: … as grounding, not as instructions.

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4. Route-result taxonomy

Every routed turn produces one of three outcomes, recorded for analytics: The raw classifier output travels on every result as raw_label, so a mysterious fallback rate in your analytics is directly diagnosable: read what the classifier actually said.

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5. How squads differ from ACD-style queues

Squads and queues answer different questions: Use a squad when one AI front door should delegate to specialists mid- conversation; use a queue when inbound work waits for a human. The two compose — a squad that escalates routes into exactly the human machinery the ACD queue model describes, and if you rank human candidates by expected outcome the predictive routing model governs that dispatch. Squads replace neither.

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6. Worked example: which specialist answers a billing question

A support squad has three members and one fallback label set: The classifier prompt, after sanitation, looks like:
Turn-by-turn:
  1. “Why was I charged twice this month?” → classifier returns "Billing." → normalized to billing → matched to the billing specialist (source: matched). The specialist receives the transcript plus a handoff packet: sentiment likely frustrated, slots extracted (the customer’s account email from earlier turns), and a summary of the thread so far.
  2. “Also, my API key returns 401s.” — same conversation, later turn → classifier returns technical → the turn re-routes to the technical specialist mid-conversation. The transcript stays one record; the new specialist reads the updated packet, including the billing answer already given, so it does not re-ask questions.
  3. “Do you sell gift cards?” → classifier returns none → no member matches → source: fallback to the general support agent.
  4. Squad daily cap reached → classification is skipped, the caller gets a structured 429, and no additional LLM spend fires — not a silent route to the fallback.

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7. Analytics: reading squad health

Squad analytics answer four operator questions per squad:
  1. Handoff success rate — of the turns handed to specialists, how many end in a resolved conversation?
  2. Drop-off — how many handed-off conversations bail to a human or error out? A high drop-off on one target is the signature of a mis-routed or under-equipped specialist.
  3. Specialist utilization — which member carries the conversation load, and which sits idle.
  4. Containment lift — does the squad contain more conversations end-to-end than the classifier agent would on its own?
Read the numbers on Agents → Squads → (squad) → Analytics or via GET /api/v1/agents/squads/{id}/analytics. Every signal derives from your existing conversation and handoff records — nothing new is written to make the charts move — and the squads run per tenant, with tenant-owned caps and content controls exactly as described under compliance posture.

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See also