Agentic AI Guardrails

Monitor AI voice agents for off-policy, unsafe, or out-of-scope behavior so you can keep automated conversations inside the guardrails you define. Voice agents are just one application. The same signals apply wherever automated or scripted conversations need to stay within defined bounds — IVR flows, AI-assisted human agents, and beyond. Use this package as a starting point — add or swap in any preset from the catalog, or define your own behaviors to fit your use case. This package includes: 2 conversation types · 6 participant roles · 8 behaviors.

Use this package

The config below is ready to use as-is — download or copy it and pass it as the config payload in a Velma Triage request. Its behaviors are preset references: the API expands each preset:<identifier> into its full definition at request time, so you don’t need the criteria inline to run the package. Download agentic-ai-guardrails.json

View / copy full config

{
  "conversation_types": [
    {
      "conversation_type_uuid": "11111111-1111-4111-8111-111111111017",
      "name": "AI Agent Customer Support Call",
      "short_description": "Any Customer Support Call with an AI agent",
      "detailed_description": "customer-supportprofessional"
    },
    {
      "conversation_type_uuid": "11111111-1111-4111-8111-111111111018",
      "name": "AI Agent Interview Monitoring",
      "short_description": "Any interview call with an AI interviewer",
      "detailed_description": "business-operationsprofessional"
    }
  ],
  "participant_roles": [
    {
      "participant_role_uuid": "22222222-2222-4222-8222-222222222004",
      "name": "Support Specialist",
      "short_description": "Someone who works with employees or contractors to solve problems with things like IT, logistics, or communication",
      "detailed_description": ""
    },
    {
      "participant_role_uuid": "22222222-2222-4222-8222-222222222012",
      "name": "Customer Service Representative",
      "short_description": "A representative from a business who is assisting a customer with an issue they are having",
      "detailed_description": ""
    },
    {
      "participant_role_uuid": "22222222-2222-4222-8222-222222222014",
      "name": "Insurance Agent",
      "short_description": "A customer support representative from an insurance company",
      "detailed_description": ""
    },
    {
      "participant_role_uuid": "22222222-2222-4222-8222-222222222015",
      "name": "Interview Candidate",
      "short_description": "Someone who is being interview for a job",
      "detailed_description": ""
    },
    {
      "participant_role_uuid": "22222222-2222-4222-8222-222222222016",
      "name": "Interviewer",
      "short_description": "A person giving an interview",
      "detailed_description": ""
    },
    {
      "participant_role_uuid": "22222222-2222-4222-8222-222222222017",
      "name": "Customer",
      "short_description": "The recipient of a service or good",
      "detailed_description": ""
    }
  ],
  "behaviors": [
    "preset:vishing",
    "preset:service_churn",
    "preset:off_topic_discussion",
    "preset:inapropriate_ai_agent_content",
    "preset:ai_agent_manipulation",
    "preset:inappropriate_speech",
    "preset:issue_resolved",
    "preset:sexual_harassment"
  ]
}

Expand the full criteria

To produce a self-contained config with every behavior’s full criteria inlined — for review, customization, or pinning a snapshot — fetch the live preset catalog and merge it into the downloaded config. The catalog is the source of truth for detection criteria.

curl -s https://platform.modulate.ai/api/velma-2-batch/list-presets \
  -H "X-API-Key: $MODULATE_API_KEY" \
| jq --slurpfile cfg agentic-ai-guardrails.json '
    [ $cfg[0].behaviors[] | ltrimstr("preset:") ] as $ids
    | { conversation_types: $cfg[0].conversation_types,
        participant_roles:  $cfg[0].participant_roles,
        behaviors: [ .presets[] | select(.identifier as $i | $ids | index($i)) ] }
  ' > agentic-ai-guardrails.full.json

import os, json, requests

cfg = json.load(open("agentic-ai-guardrails.json"))
ids = {b.removeprefix("preset:") for b in cfg["behaviors"]}

catalog = requests.get(
    "https://platform.modulate.ai/api/velma-2-batch/list-presets",
    headers={"X-API-Key": os.environ["MODULATE_API_KEY"]},
).json()["presets"]

cfg["behaviors"] = [p for p in catalog if p["identifier"] in ids]
json.dump(cfg, open("agentic-ai-guardrails.full.json", "w"), indent=2)

agentic-ai-guardrails.full.json keeps the same conversation_types and participant_roles and replaces each preset reference with its full behavior definition — drop it into the config payload exactly like the preset version.

Conversation types

The interaction contexts this package expects to see.

Name	What it is
AI Agent Customer Support Call	Any Customer Support Call with an AI agent
AI Agent Interview Monitoring	Any interview call with an AI interviewer

Participant roles

The speaker roles the package distinguishes.

Name	What it is
Support Specialist	Someone who works with employees or contractors to solve problems with things like IT, logistics, or communication
Customer Service Representative	A representative from a business who is assisting a customer with an issue they are having
Insurance Agent	A customer support representative from an insurance company
Interview Candidate	Someone who is being interview for a job
Interviewer	A person giving an interview
Customer	The recipient of a service or good

Behaviors

The 8 signals this package detects. Each maps to a reusable preset:<identifier> you can drop into the behaviors array of any BatchConfig — the config above already references them.

Full detection criteria are not duplicated here. The live preset catalog is the source of truth — retrieve the exact criteria for any behavior by name from the list-presets endpoint.

Behavior	What it detects	Preset
Vishing	Attempts to elicit sensitive information through deceptive voice interactions. We detect vishing based on abnormal call pacing, probing question patterns, stress-induced pitch shifts, and background noise suggesting call centers or spoofed environments.	`preset:vishing`
Service Churn	Customer decides to cancel an ongoing service. We detect this through resignation tone, conclusive phrasing, disengaging cadence, and emotional withdrawal.	`preset:service_churn`
Off-topic Discussion	Conversation largely unrelated to call purpose. We detect this using semantic drift paired with relaxed pacing, reduced task-oriented urgency, and tonal divergence from initial intent.	`preset:off_topic_discussion`
Inapropriate AI Agent Content	AI-generated speech unsuitable for professional contexts. We detect this by tonal incongruence with task intent, inappropriate emotional expression, unnatural phrasing patterns, and drift into unrelated or personal topics.	`preset:inapropriate_ai_agent_content`
AI Agent Manipulation	Attempts to coerce an AI into unintended behavior. We detect this using repetitive prompt pressure, altered speaking cadence, strategic pauses, adversarial tone, and escalating emotional manipulation signals across the interaction.	`preset:ai_agent_manipulation`
Inappropriate Speech	Unprofessional or unsuitable spoken content. We detect this using aggressive tone, boundary-crossing language, emotional volatility, and contextual mismatch with professional norms.	`preset:inappropriate_speech`
Issue Resolved	Customer’s problem successfully addressed. We detect this using relief markers, positive tonal shift, relaxed pacing, confirmation language, and conversational closure cues.	`preset:issue_resolved`
Sexual Harassment	Unwanted sexualized speech or advances. We detect this through suggestive intonation, boundary-testing pauses, inappropriate familiarity, and discomfort responses from others.	`preset:sexual_harassment`

​Use this package

​Expand the full criteria

​Conversation types

​Participant roles

​Behaviors

Use this package

Expand the full criteria

Conversation types

Participant roles

Behaviors