How to Use Relevance AI for Single-goal agent runs

Deliver maximum ROI through optimized specialist agents. Build agents for sales, customer success, marketing and HR, and run millions of daily tasks on one platform. Confirm live details on relevanceai.com before production use.

Practical Single-goal agent runs examples

Example 1

Scenario:
Relevance AI — Single-goal agent runs (pass 1). Context: Deliver maximum ROI through optimized specialist agents. Build agents for sales, customer success, marketing and HR, and run millions of dai

Objective:
Deliver a reviewable single-goal agent runs result using Relevance AI.

Inputs:
- Verified facts from relevanceai.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents

Workflow:
Open Relevance AI → Configure for single-goal agent runs → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Relevance AI capabilities; do not invent features.
- Confirm live details on relevanceai.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete single-goal agent runs artifact plus a short verification checklist.

Example 2

Scenario:
Relevance AI — Single-goal agent runs (pass 2). Context: Deliver maximum ROI through optimized specialist agents. Build agents for sales, customer success, marketing and HR, and run millions of dai

Objective:
Deliver a reviewable single-goal agent runs result using Relevance AI.

Inputs:
- Verified facts from relevanceai.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents

Workflow:
Open Relevance AI → Configure for single-goal agent runs → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Relevance AI capabilities; do not invent features.
- Confirm live details on relevanceai.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete single-goal agent runs artifact plus a short verification checklist.

Example 3

Scenario:
Relevance AI — Single-goal agent runs (pass 3). Context: Deliver maximum ROI through optimized specialist agents. Build agents for sales, customer success, marketing and HR, and run millions of dai

Objective:
Deliver a reviewable single-goal agent runs result using Relevance AI.

Inputs:
- Verified facts from relevanceai.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents

Workflow:
Open Relevance AI → Configure for single-goal agent runs → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Relevance AI capabilities; do not invent features.
- Confirm live details on relevanceai.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete single-goal agent runs artifact plus a short verification checklist.

Example 4

Scenario:
Relevance AI — Single-goal agent runs (pass 4). Context: Deliver maximum ROI through optimized specialist agents. Build agents for sales, customer success, marketing and HR, and run millions of dai

Objective:
Deliver a reviewable single-goal agent runs result using Relevance AI.

Inputs:
- Verified facts from relevanceai.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents

Workflow:
Open Relevance AI → Configure for single-goal agent runs → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Relevance AI capabilities; do not invent features.
- Confirm live details on relevanceai.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete single-goal agent runs artifact plus a short verification checklist.

Example 5

Scenario:
Relevance AI — Single-goal agent runs (pass 5). Context: Deliver maximum ROI through optimized specialist agents. Build agents for sales, customer success, marketing and HR, and run millions of dai

Objective:
Deliver a reviewable single-goal agent runs result using Relevance AI.

Inputs:
- Verified facts from relevanceai.com
- Audience, channel, or technical constraints
- Success criteria and forbidden claims
- Relevant product surfaces: Agents

Workflow:
Open Relevance AI → Configure for single-goal agent runs → Pilot with sample inputs → Review against success criteria → Iterate one axis → Finalize

Requirements:
- Use only verified Relevance AI capabilities; do not invent features.
- Confirm live details on relevanceai.com before promising volume or pricing.
- Human-review before external publish, send, billing, or compliance use.

Expected output:
A concrete single-goal agent runs artifact plus a short verification checklist.

Checklist before you ship

• Confirm the workflow stays inside verified Relevance AI capabilities
• Review outputs against relevanceai.com when accuracy or pricing claims matter
• Keep a short verification list for any claim you would publish externally