Now tracking answers across ChatGPT, Claude, Perplexity & Gemini

Curated agentic GTM workflows

Signals become useful when an agent can act on them.

These workflows connect Limelit's visibility, perception, citation, and crawler evidence to a reviewed action loop. The agent does the repetitive research and drafting; a person remains accountable for what ships.

ACTION loopDetectDraftReviewPublishMeasure
01Brand perception

Correct an answer-engine misconception

Outcome

A sourced correction brief and reviewed owned-source response tied to the original answer evidence.

Find a hallucinated or negative claim, ground a correction in the original answer, and route it through editorial review before distribution.

Example agent excerpt

“I found the disputed claim, linked the answer evidence, and prepared a factual correction for your review. Nothing has been published.”

Setup steps
  1. Connect Limelit MCP with a personal access token.
  2. Select the active property and ask the agent to list misinformation.
  3. Open the linked correction draft and verify every claim before publishing.
  1. Detect

    List misinformation and inspect the cited answer evidence.

  2. Draft

    Create channel-specific correction copy from verified product facts.

  3. Review

    Check the correction against the source answer and approved product facts.

  4. Publish

    Approve and post to the owned source most likely to be recrawled.

Workflow pathtool:list_misinformation → UI:correction draft → human:review → human:publish correction
02Competitive content

Turn a competitor citation gap into an article

Outcome

A source-backed article draft aimed at a measured competitor citation gap, ready for editorial review.

Trace where a competitor earns citations that your brand misses, then produce a source-backed article aimed at the same buyer question.

Example agent excerpt

“The strongest gap is on implementation questions. These three cited sources support an article brief; I drafted it without inventing results.”

Setup steps
  1. Track the competitors and buyer questions that define the comparison set.
  2. Run find_easy_wins and inspect the cited-source evidence.
  3. Generate a draft, then send it through the normal editorial approval path.
  1. Detect

    Compare top sources, prompt results, and competitor movement.

  2. Draft

    Generate an article from the strongest source and opportunity brief.

  3. Review

    Verify citations, claims, and fit with the measured buyer question.

  4. Publish

    Require a human review, then publish and monitor later answers.

Workflow pathtool:find_easy_wins → tool:generate_draft → human:review → tool:publish_draft
03Agent analytics

Recover pages that AI agents cannot retrieve

Outcome

A prioritized remediation brief that separates failed high-value paths from healthy crawler traffic.

Separate healthy crawler demand from failed requests, prioritize valuable paths, and hand a precise remediation brief to the site owner.

Example agent excerpt

“This cited page was never crawled, while this frequently crawled path returned errors. I ranked the two fixes and included the supporting counts.”

Setup steps
  1. Connect a log source from the Agents setup flow.
  2. Preview and confirm known-bot records for the selected property.
  3. Compare crawl errors with the crawl-to-citation join before proposing a fix.
  1. Detect

    Rank crawl failures and compare retrievals with citations by URL.

  2. Draft

    Propose the smallest routing, robots, or server-side correction.

  3. Review

    Have the site owner verify impact, scope, and rollback before code changes.

  4. Publish

    Ship through the normal code review path and verify new visits.

Workflow pathtool:list_crawl_errors → tool:get_crawl_citation_join → human:code review → human:deploy fix
04Campaign intelligence

Measure whether a launch changed AI visibility

Outcome

A bounded before-and-after campaign brief with freshness caveats and no unsupported causal claim.

Compare supported pre- and post-launch signals without inventing causality, then turn the strongest gap into a follow-up asset.

Example agent excerpt

“Visibility moved after launch, but the data only supports correlation. Here are the source and engine changes, plus the incomplete-coverage caveat.”

Setup steps
  1. Track the launch questions and any campaign URLs before analysis.
  2. Run the campaign_tracker prompt with the launch date and comparison window.
  3. Review freshness and coverage caveats before creating a follow-up asset.
  1. Detect

    Run the campaign tracker across the declared launch window.

  2. Draft

    Build a follow-up brief from measured engine and source changes.

  3. Review

    Challenge causal language and confirm both windows have comparable coverage.

  4. Publish

    Approve the asset, publish it, and repeat on the next full window.

Workflow pathprompt:campaign_tracker → tool:list_top_sources → tool:generate_draft → human:review → tool:publish_draft
05Research operations

Keep the buyer-question portfolio useful

Outcome

A reviewed prompt-change set with a reason for every addition, edit, or removal.

Audit tracked prompts for stale coverage, duplication, and missing funnel stages before changing the evaluation set.

Example agent excerpt

“Two prompts overlap, one engine has stale coverage, and the comparison stage is missing. I proposed three changes and left the set untouched.”

Setup steps
  1. Select the property and confirm its tracked competitors.
  2. Run the prompt_grader prompt against the full active and inactive set.
  3. Approve each recommendation before calling a prompt mutation tool.
  1. Detect

    Grade the portfolio against run freshness and current outcomes.

  2. Draft

    Propose additions, edits, and removals with a reason for each.

  3. Review

    Confirm that every change preserves intended funnel and engine coverage.

  4. Publish

    Approve the set, apply changes, and reevaluate affected prompts.

Workflow pathprompt:prompt_grader → human:approve → tool:update_prompt → tool:reevaluate_prompt
06Product narrative

Respond to a negative perception trend

Outcome

A factual FAQ, comparison, or proof page grounded in the attributes and answer excerpts behind the trend.

Connect a sentiment drop to the exact attributes and answer evidence behind it, then publish a factual page that resolves the underlying confusion.

Example agent excerpt

“The decline clusters around onboarding complexity. I linked the supporting excerpts and drafted an FAQ using only the approved product facts.”

Setup steps
  1. Run fresh evaluations for the buyer questions you want to monitor.
  2. Inspect sentiment, brand attributes, and their supporting answer snippets.
  3. Choose the response format and verify each product claim before publishing.
  1. Detect

    Inspect sentiment series, attributes, and supporting snippets.

  2. Draft

    Create a concise FAQ, comparison, or product-proof article.

  3. Review

    Match every drafted claim to approved facts and the cited answer evidence.

  4. Publish

    Review every claim, publish, and watch subsequent engine runs.

Workflow pathtool:get_sentiment_series → tool:list_brand_attributes → tool:generate_draft → human:review → tool:publish_draft

Builder submissions

Built a loop worth sharing?

Send the workflow, its agent path, and evidence that it works. Submissions enter editorial review; they never publish directly to this gallery.

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