Customer Support Agents
Ground chat, voice, and case assistants in approved policy so refunds, disputes, and disclosures stay accurate under pressure.
Ground chat, voice, and case assistants in approved policy so refunds, disputes, and disclosures stay accurate under pressure.
4 field failure modes become adversarial campaigns tailored to this deployment.
Runtime Security, Automated Red Teaming keep the workflow bounded after launch.
Built for this workflow
# Apply the solution playbook. # $ ga solutions apply support-agents deployment: support-agents assets: - CRM - knowledge bases - voice transcripts test_against: - Hallucinated refunds and illegal advice - Voice errors became late-night fodder - Prompt-jacked bots embarrassed brands runtime_controls: - AI Runtime Security - Automated AI Red Teaming evidence: traces,citations,owners
Field evidence
Customer Support Agents deployments fail when the model gets more trust than the workflow can safely absorb. These examples become concrete tests, not generic awareness copy.
Air Canada's bot invented a bereavement fare that did not exist and New York City's MyCity assistant told businesses they could violate labor and health codes—both organizations were held accountable for the bad guidance.
McDonald's drive-thru pilot repeatedly heard “200 McNuggets” and added bacon to sundaes until the company halted the rollout with its vendor, proving how unforgiving live audio can be.
Pranksters convinced a Chevrolet dealer's bot to “sell” a $76k Tahoe for $1 and got DPD's chatbot to swear and insult its own employer before both companies killed the experience.
xAI's Grok shipped antisemitic rants and even instructions on committing crimes, forcing the company to apologize and pause posts—showing that unmoderated responses spread instantly.
How General Analysis helps
The playbook connects discovery, automated red teaming, and runtime protection so controls stay specific to the deployment instead of becoming a generic policy layer.
Ground every turn in approved KBs, require citations, enforce confidence checks, and run tone + offer filters derived from red-team findings.
Hammer bots with abusive language, refund scams, hidden instructions, and off-menu orders so you can tune refusals and escalation trees before launch.
Inventory CRM, knowledge bases, voice transcripts and the identities, tools, and data paths attached to the workflow.
Turn field failures into adversarial prompts, multi-turn tests, tool-use probes, and policy traps for this deployment.
Apply policy-grounded replies, confidence-based escalation, and escalation rules where the workflow needs them.
Conversation traces with cited sources
FAQ
Practical answers for deploying customer support agents with controls that security, legal, and operators can inspect.
Runtime Protection enforces a strict grounding requirement: every response must be backed by content from your curated knowledge bases and must include inline citations to the specific policy document, section, or FAQ entry it draws from. When the retrieval engine finds no authoritative source for a customer question, the system blocks the completion entirely and routes the case to a human agent queue with the full conversation context attached. This prevents the bot from improvising answers about refund policies, warranty terms, or account rules that could create contractual liability.