AI AGENTS
Validate a Drafted RFP Answer Against the Current Library
On manual submission of a proposed RFP answer, an agent checks it against the curated knowledge library for outdated claims or missing citations and returns a pass or fix-it…
How it runs
The automated pipeline, trigger to output.
- TriggerWriter submits a drafted answer manually
- ActionExtract factual claims from the draft via OpenAIOpenAI
- ActionLook up current approved statements in ConfluenceConfluence
- ActionMark each claim supported, conflicting, or uncitedOpenAI
- LogicSet verdict to pass only if no conflicts
- OutputPost per-claim verdict and citations to SlackSlack
What it does
Acts as a reviewer before an RFP answer goes to a buyer. A proposal writer submits a drafted answer; the agent compares every factual claim against your curated Confluence library, flags statements that conflict with or are unsupported by current approved content, and returns a pass-or-revise verdict with the exact citations to fix.
When to use it
Use it as the final quality gate when answers are hand-written or pulled from older proposals, to catch stale numbers, deprecated features, or uncited claims before they reach a prospect.
How it works
- 1A proposal writer submits a drafted answer through a manual run.
- 2The agent extracts each discrete factual claim from the draft.
- 3It searches the Confluence answer library for the current approved statement on each claim.
- 4OpenAI compares draft claims to library content, marking each supported, conflicting, or uncited.
- 5A logic step sets the overall verdict to pass only if no conflicts remain.
- 6The agent posts a per-claim Slack report with verdicts and the citations needed to fix issues.
Set it up
What you configure once, before turning it on.
- 1Connect ConfluenceSpaces, pages, blueprints.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.