Doormile Agent Layer

The model proposes. The solver decides. The system validates.

Every capability below is an application of that one sentence. A language model is exceptional at turning mess into structure — a dispatcher's sentence into a constraint, a WhatsApp forward into a booking, a photo into a condition code, a solver's output into an explanation a rider will actually read. It is poor and expensive at choosing the best of ten thousand arrangements, which is exactly what dispatch is.

So "auto-assign with GenAI" does not mean a model picking riders. It means a model doing the four things around the pick that were previously impossible to automate, while a solver does the pick itself in milliseconds and a validator refuses anything that breaks an invariant. That is a more capable system than an LLM choosing alone, and a cheaper one.

Ten capabilities, ranked by what they return for what they cost. Note how few of them are "the AI decides" and how many are "the AI turns something unstructured into something the system can already handle."

Four hubs across Coimbatore, Hyderabad, Bengaluru and Chennai means four language regions. Item 8 is not a nicety in that footprint — a rider who can speak a status update is a rider who actually files one.

This is the thing you asked about, drawn end to end. The deterministic spine runs down the left and never waits on a model. The three GenAI touch points sit beside it, feeding in out of band.

Why the constraint compiler is the sleeper

Dispatchers hold dozens of rules in their heads that never reach the system: which rider is slow at a particular gate, which client insists on the same face, which lane floods in monsoon. Today the only way to encode those is a developer ticket, so they stay in the dispatcher's head and the optimiser looks stupid. A model that turns a typed sentence into a reviewable constraint — which a human approves before it ever binds — converts tacit knowledge into solver input at conversational speed. That is a capability the solver cannot have on its own, and it is where generative AI is doing something genuinely irreplaceable.

// what the compiler emits — reviewed by a human, then stored
{
  "type": "exclude_miler_from_location_class",
  "miler_id": 412,
  "location_class": "mall_service_gate",
  "reason": "slow at gate handover",
  "author": "dispatcher:coimbatore:7",
  "expires_at": null
}

You do not need new infrastructure. Every piece below already exists in the estate; the work is contracts and placement.

One deliberate constraint: the agent layer never writes to the database directly. It calls the same internal endpoints a human operator would, so tenant scoping, idempotency, validation and audit apply identically whether the caller is a person or a model. If an agent can reach a table your dispatcher cannot, you have built two systems and will secure only one.

Every capability climbs the same four rungs, and each rung has a numeric gate. Nothing goes to the next rung on a good feeling.

This is the sharpest finding in the codebase, and it makes everything above cheaper.

• Closed-set selection, enforced server-side. A model returning chosen_miler_id must have that ID checked against the candidate set before commit. Models hallucinate identifiers; a validator costs three lines and removes the entire failure class.
• Keep the payload structured. Your current request to routemate carries no customer free text. The moment you pass raw Notes, address strings or client emails into a prompt that also drives actions, you have built a prompt-injection channel — anyone who can type into a booking form can try to instruct your dispatcher. If unstructured text must be read, do it in a separate call that only extracts, and never let that call's output reach an action without validation.
• Tenant isolation is an invariant, not a preference. HubStaffAccount.Tenantid already isolates partner freight. Agents must be scoped identically, and cross-tenant leakage should be a hard validator failure, not a scoring penalty. This is the one mistake that is a breach rather than a bad route.
• Every agent action is idempotent. You already have the Redis-backed key infrastructure. Agents retry more than humans do; a duplicated reassignment or a double COD entry is far worse than a slow one.
• Budget per booking, enforced. Set a token ceiling per decision and per day, with a hard cutoff to the deterministic path. Cost overruns in agentic systems come from retry storms, not from steady state.
• Mind what leaves the estate. Consignee phone numbers, COD amounts and precise home coordinates are in scope for whatever model endpoint you call. Decide deliberately what is sent, and prefer IDs over identities wherever the model does not need the human detail.
• Escalate rather than improvise. Give the exception agent an explicit "page a human" tool and reward using it. An agent with no honourable way to stop will invent an action instead.