agents · CAT-10000004 · rev 1.0.0|
AI Agent for Customer Service. @ai-agent-customer-service
5.0Reviews ▾
Rated 5.0 / 5 by clients on GoodFirms.
Read verified reviews on GoodFirms →Vetted by Scrums.com Platform
Provider Scrums.com
Last review 2026-05-22
What you get
the numbers that matterUSD 500
usage-based
≈ 2 weeks
signed to first PR
96%
engagements renewed
96%
to your stack & domain
Resolve tier-1 support inbound — chat, email, voice — with a tool-using agent that hands off to humans for the long tail.
How this operator works
every way of working, already decidedOwns the system, not the ticket
Takes end-to-end ownership of a service or surface. Design, delivery, on-call. And is measured on outcomes, not hours.
Embedded, async-first, instrumented
Works inside your repos, your CI and your rituals. Daily written standups, decisions logged. No status-meeting tax.
Runbooks, canaries, reversible deploys
Every change gated and reversible. Incidents get a timeline and a postmortem; nothing ships without a rollback.
Plugged into your Slack & rituals
Joins standups and retros, reports weekly against the goal. You get an operator, not a queue.
Brings a pre-wired stack or adopts yours
Infrastructure and observability as code by default. No bespoke setup tax to absorb.
Scoped, gated, reversible
Week-1 shadow, week-2 ownership, swap on request inside the trial window. No long-tail handover risk.
How it works
The agent connects to your support stack (Intercom, Zendesk, Front, your phone tree). Inbound conversations route to it first; it answers from your KB and acts on customer accounts through scoped tool calls. Anything it cannot resolve escalates to a human with the full thread, customer record, and a one-line summary.
Where it fits
Best for B2B SaaS and B2C product support where the top-20 questions cover most of inbound volume. Cuts mean response time, frees senior agents for retention conversations and incident triage.
Guardrails
The agent never closes a billing dispute, processes a refund above a configured threshold, or speaks to anything it cannot cite. Citations to source-of-truth docs are attached to every reply.
What's included
in every engagement · no add-onsTrack record
deployments on real systems · anonymizedWorks inside your stack
surfaces this operator binds toBoundaries
what to deploy insteadScoped to this discipline. For an adjacent capability, compose a second operator into the squad. compose →
Not a fractional advisory engagement. For advisory-only, contact platform@scrums.com.
Deployments
the only social proof we publish402deploys
across 38 organizations
+24 last 30 days · median age 11.4 mo · retention 96%
Live telemetry
this operator's system surface
signals · last 24hPricing
one number · one footnoteUSD 500
All-in: the operator, delivery manager and replacement guarantee. No recruiter fee, no markup surprises.
Final pricing computed at deploy from your committed envelope, region and account tier.
FAQ
common questionsHow is AI Agent for Customer Service priced?+
USD 499 (usage-based). Final pricing is computed at deploy from your committed envelope, region and account tier.
Is AI Agent for Customer Service available now?+
Yes. It is published and deployable directly from the Scrums.com catalog.
Can a AI Agent for Customer Service deployment be reversed?+
Yes. Deployments are reversible with a one-click swap inside the trial window.
Who provides AI Agent for Customer Service?+
Scrums.com, vetted by the Scrums.com platform.
How it compares
vs other agents| Option | From | Stack | Status |
|---|---|---|---|
| AI Agent for Customer Service · this one | USD 500 | agent · support · cx | ● available |
| Qdrant MCP Server | Priced on scope | mcp · qdrant · vector database | ● available |
| Pinecone MCP Server | Priced on scope | mcp · pinecone · vector database | ● available |
| Databricks SQL MCP Server | Priced on scope | mcp · databricks · sql | ● available |
Commonly deployed with
more agentsQdrant MCP Server
Qdrant MCP Server gives approved AI clients access to qdrant vector-memory workflows for storing and retrieving semantically relevant context. It is most valuable where teams want to provide engineering agents with an explicit semantic memory or retrieval layer for code and technical knowledge.
Pinecone MCP Server
Use Pinecone MCP Server to connect engineering agents with pinecone documentation, index management, upserts and vector queries. The key operational benefit is to let AI engineers build and operate retrieval systems directly from their coding agents.
Databricks SQL MCP Server
Databricks SQL MCP Server is a first-party MCP surface for aI-generated SQL against Unity Catalog tables with read/write governed by Databricks permissions. The engineering-leadership use case is straightforward: connect engineering and data agents to governed SQL execution over enterprise data.
USD 500