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How to Use Clay for AI research agent workflows
Learn Clay ai research agent workflows with step by step workflows, realistic examples, and verified plan notes.
Clay works well for ai research agent workflows when you run it like production work: locked brief, SOURCE facts, then review before publish. Clay provides access to 150+ premium data sources and AI research agents, then automates growth workflows. Credit usage varies by enrichment; enterprise pricing is custom. Verify contacts on a sample before campaigns. Start at /explore/clay.
This guide focuses on ai research agent workflows in detail. Related Clay articles: /blog/how-to-use-clay-for-growth-automation-playbooks, /blog/how-to-use-clay-for-email-verification-samples, /blog/how-to-use-clay-for-credit-spend-monitoring.
When this workflow is the right job
Use ai research agent workflows when the deliverable is specifically this Clay job. Switch to multi-source enrichment tables when that workflow already owns the asset.
Step by step workflow
1. Brief AI research agent workflows
Write what must stay true for ai research agent workflows in Clay before settings or spend.
Brief: AI research agent workflows Keep: verified SOURCE facts only Avoid: invented pricing or features Success: one reviewable output
2. Open Clay for AI research agent workflows
Use the Clay surface that owns ai research agent workflows. Do not mix a neighboring workflow in the same pass.
Surface: AI research agent workflows Start: pilot with one representative input Plans: www.clay.com
3. Pilot AI research agent workflows
Run a single ai research agent workflows pilot. Score clarity, grounding, and whether the output is reviewable.
Pilot: AI research agent workflows [ ] SOURCE facts match [ ] Output reviewable [ ] Settings logged
4. Refine AI research agent workflows
Change one ai research agent workflows dimension only. Save a template from the best run.
Refine: AI research agent workflows Change: one control only Keep: SOURCE and success criteria
Practical ai research agent workflows examples
Field map
Scenario: A GTM operator runs AI research agent workflows in Clay for "Field map". Objective: Produce a credit-aware automation result for Field map that is safe to scale. Inputs: - Source/query for Field map - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Field map → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Field map with credit usage noted and duplicates removed.
Credit budget
Scenario: A GTM operator runs AI research agent workflows in Clay for "Credit budget". Objective: Produce a credit-aware automation result for Credit budget that is safe to scale. Inputs: - Source/query for Credit budget - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Credit budget → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Credit budget with credit usage noted and duplicates removed.
Webhook trigger
Scenario: A GTM operator runs AI research agent workflows in Clay for "Webhook trigger". Objective: Produce a credit-aware automation result for Webhook trigger that is safe to scale. Inputs: - Source/query for Webhook trigger - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Webhook trigger → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Webhook trigger with credit usage noted and duplicates removed.
Filter title
Scenario: A GTM operator runs AI research agent workflows in Clay for "Filter title". Objective: Produce a credit-aware automation result for Filter title that is safe to scale. Inputs: - Source/query for Filter title - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Filter title → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Filter title with credit usage noted and duplicates removed.
Company size
Scenario: A GTM operator runs AI research agent workflows in Clay for "Company size". Objective: Produce a credit-aware automation result for Company size that is safe to scale. Inputs: - Source/query for Company size - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Company size → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Company size with credit usage noted and duplicates removed.
Export CSV free
Scenario: A GTM operator runs AI research agent workflows in Clay for "Export CSV free". Objective: Produce a credit-aware automation result for Export CSV free that is safe to scale. Inputs: - Source/query for Export CSV free - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Export CSV free → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Export CSV free with credit usage noted and duplicates removed.
Schedule daily
Scenario: A GTM operator runs AI research agent workflows in Clay for "Schedule daily". Objective: Produce a credit-aware automation result for Schedule daily that is safe to scale. Inputs: - Source/query for Schedule daily - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Schedule daily → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Schedule daily with credit usage noted and duplicates removed.
Error alert
Scenario: A GTM operator runs AI research agent workflows in Clay for "Error alert". Objective: Produce a credit-aware automation result for Error alert that is safe to scale. Inputs: - Source/query for Error alert - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Error alert → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Error alert with credit usage noted and duplicates removed.
Owner column
Scenario: A GTM operator runs AI research agent workflows in Clay for "Owner column". Objective: Produce a credit-aware automation result for Owner column that is safe to scale. Inputs: - Source/query for Owner column - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Owner column → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Owner column with credit usage noted and duplicates removed.
Stop on empty
Scenario: A GTM operator runs AI research agent workflows in Clay for "Stop on empty". Objective: Produce a credit-aware automation result for Stop on empty that is safe to scale. Inputs: - Source/query for Stop on empty - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Stop on empty → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Stop on empty with credit usage noted and duplicates removed.
Preview 10 rows
Scenario: A GTM operator runs AI research agent workflows in Clay for "Preview 10 rows". Objective: Produce a credit-aware automation result for Preview 10 rows that is safe to scale. Inputs: - Source/query for Preview 10 rows - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Preview 10 rows → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Preview 10 rows with credit usage noted and duplicates removed.
Enrich email only
Scenario: A GTM operator runs AI research agent workflows in Clay for "Enrich email only". Objective: Produce a credit-aware automation result for Enrich email only that is safe to scale. Inputs: - Source/query for Enrich email only - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Enrich email only → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Enrich email only with credit usage noted and duplicates removed.
CRM handoff
Scenario: A GTM operator runs AI research agent workflows in Clay for "CRM handoff". Objective: Produce a credit-aware automation result for CRM handoff that is safe to scale. Inputs: - Source/query for CRM handoff - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate CRM handoff → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for CRM handoff with credit usage noted and duplicates removed.
Rate limit polite
Scenario: A GTM operator runs AI research agent workflows in Clay for "Rate limit polite". Objective: Produce a credit-aware automation result for Rate limit polite that is safe to scale. Inputs: - Source/query for Rate limit polite - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Rate limit polite → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Rate limit polite with credit usage noted and duplicates removed.
Template clone
Scenario: A GTM operator runs AI research agent workflows in Clay for "Template clone". Objective: Produce a credit-aware automation result for Template clone that is safe to scale. Inputs: - Source/query for Template clone - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Template clone → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Template clone with credit usage noted and duplicates removed.
Scrape LinkedIn
Scenario: A GTM operator runs AI research agent workflows in Clay for "Scrape LinkedIn". Objective: Produce a credit-aware automation result for Scrape LinkedIn that is safe to scale. Inputs: - Source/query for Scrape LinkedIn - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Scrape LinkedIn → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Scrape LinkedIn with credit usage noted and duplicates removed.
Enrich 3 credits
Scenario: A GTM operator runs AI research agent workflows in Clay for "Enrich 3 credits". Objective: Produce a credit-aware automation result for Enrich 3 credits that is safe to scale. Inputs: - Source/query for Enrich 3 credits - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Enrich 3 credits → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Enrich 3 credits with credit usage noted and duplicates removed.
Sheet export
Scenario: A GTM operator runs AI research agent workflows in Clay for "Sheet export". Objective: Produce a credit-aware automation result for Sheet export that is safe to scale. Inputs: - Source/query for Sheet export - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Sheet export → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Sheet export with credit usage noted and duplicates removed.
Deduped leads
Scenario: A GTM operator runs AI research agent workflows in Clay for "Deduped leads". Objective: Produce a credit-aware automation result for Deduped leads that is safe to scale. Inputs: - Source/query for Deduped leads - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Deduped leads → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Deduped leads with credit usage noted and duplicates removed.
GTM playbook
Scenario: A GTM operator runs AI research agent workflows in Clay for "GTM playbook". Objective: Produce a credit-aware automation result for GTM playbook that is safe to scale. Inputs: - Source/query for GTM playbook - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate GTM playbook → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for GTM playbook with credit usage noted and duplicates removed.
Sandbox first
Scenario: A GTM operator runs AI research agent workflows in Clay for "Sandbox first". Objective: Produce a credit-aware automation result for Sandbox first that is safe to scale. Inputs: - Source/query for Sandbox first - Field map - Credit budget - Destination (sheet/CRM) Workflow: Sandbox scrape/enrich → Validate Sandbox first → Deduplicate → Export → Monitor credits Requirements: - Stay within verified Clay capabilities; do not invent features. - Confirm live plan notes on www.clay.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. - Preview a small batch before spending credits at scale. Expected output: A clean export for Sandbox first with credit usage noted and duplicates removed.
How to improve ai research agent workflows
Cut noise from ai research agent workflows by removing extra adjectives while preserving SOURCE facts in Clay.
Raise quality by insisting on a single success check before debating style.
Make review easier by labeling fields that must never change.
Speed iteration by cloning the last good run and altering only one control.
Stabilize outputs by pinning settings after the pilot is approved.
Reduce rework by rejecting drafts that invent claims.
Improve handoffs by recording which control produced the best result.
Harden the workflow by testing an incomplete input before trusting defaults.
Prompting and usage guidance
Name the ai research agent workflows job, audience, and success check before opening Clay.
Paste only verified facts under SOURCE so Clay cannot invent details.
Specify the deliverable shape up front.
Call out fixed details versus flexible style choices.
Ask Clay to flag unsupported claims before you accept the draft.
Limitations to respect
Check Clay plan gates for ai research agent workflows on www.clay.com before you promise timelines.
Keep drafts unpublished until a human confirms SOURCE facts.
Clay can be wrong. Treat ai research agent workflows as provisional until review.
If documentation is silent on a claim, leave it out rather than guessing.
Practical tips for this workflow
Pilot once before batching ai research agent workflows in Clay.
Keep a reusable template with variables for ai research agent workflows.
Separate creative instructions from SOURCE facts.
Log settings from the best run.
Common mistakes
- Skipping the pilot run before scaling volume
- Inventing pricing, quotas, or features not on official pages
- Mixing unrelated workflows in one session
- Publishing without a human review gate
Treat ai research agent workflows in Clay as a production workflow: brief, pilot, refine, then ship with review. Related reading: /blog/how-to-use-clay-for-growth-automation-playbooks, /blog/how-to-use-clay-for-email-verification-samples, /blog/how-to-use-clay-for-credit-spend-monitoring.