Live custom AI agents
Three agents were turned from workflow problems into published, usable Notion agents with clear inputs, structured outputs, and business use cases.
Overview
Role
Founder · Custom AI Agent Strategy · Workflow Design · Prompt Architecture · Output Design
Platform
Published custom agents for Notion Marketplace
Status
Live / published agent listings
Deliverables
Agent concepts, workflow logic, prompt architecture, structured output design, use-case framing, icon direction, gallery assets
Product system
Messy input → extraction → decision logic → risks/actions → handoff-ready output
Custom AI agents built for usable workflow outputs, not vague chat.
The agents are designed around a practical gap: teams already have the information they need, but it is often buried inside briefs, emails, call notes, SOWs, proposals, client comments, and scattered campaign notes.
Instead of asking a general chatbot to “make sense of this,” each agent has a defined job: extract the important details, organise the mess, highlight risks or missing information, and turn it into a clear output that creators, agencies, marketing teams, consultants, and client-facing operators can act on quickly.
Founder Insight
Useful AI does not start with a blank chat. It starts with a repeatable workflow and a clear output.
The strongest use case was not “AI that can do anything.” It was AI that understands a specific operational moment: a brief needs turning into deliverables, client notes need turning into a request plan, or feedback needs turning into revision actions.
That made the product direction sharper. Each agent is intentionally narrow enough to be useful, but flexible enough to work with the messy real-world inputs teams already have.
This is the commercial opportunity: I can design AI agents around the way a business already works, define the output before the prompt, and package the result so the user understands what to give the agent and what they will get back.
Product Highlights
A small agent suite for high-friction business handoffs.
The suite focuses on the practical admin layer that slows teams down: brief translation, client request planning, revision summaries, weekly updates, and turning unstructured notes into clean next steps. It shows how custom AI agents can be scoped around real work instead of broad, generic “assistant” promises.
UGC brief-to-delivery
Campaign notes and approved creative angles become hooks, openings, scripts, shot lists, checklists, and filming-ready deliverables.
Client request planning
Briefs, emails, meeting notes, proposals, and SOWs become a clear request plan with scope, approvals, risks, dependencies, and missing information.
Revision and update workflows
Feedback, review notes, Loom comments, Slack messages, and campaign notes become structured revision summaries, blockers, next steps, and polished weekly client updates.
Agent Suite
Three agents, three practical workflow moments.
Each agent is positioned around a specific operational handoff: what messy information goes in, what structure comes out, and who can use the result. That clarity is what makes the agents feel like products, not prompts.
Client Request Architect
Turns scattered client inputs into a request plan covering the core request, scope, deliverables, risks, approvals, dates, dependencies, missing information, and next steps.
View Notion profile ↗
UGC Playbook Brief-to-Delivery Agent
Turns existing briefs, campaign notes, client inputs, and approved angles into hooks, openings, shot lists, scripts, checklists, and filming-ready deliverables.
View Notion profile ↗
UGC Brief-to-Delivery Agent
Turns messy UGC campaign information into deliverables, feedback-to-revision summaries, blockers, next steps, and weekly client updates for creators, agencies, and marketing teams.
View Notion profile ↗Challenge & Solution
The opportunity was not to make AI sound impressive. It was to make messy work easier to hand off.
The product direction had to feel useful to teams who already use Notion and already understand the pain of scattered input. The agents needed to be clear, specific, and immediately practical — with enough structure that a user could trust the output without rebuilding it from scratch.
The challenge
- Client information arrives in fragments across emails, calls, briefs, proposals, SOWs, messages, and campaign notes.
- Generic AI chat can produce text, but often leaves teams still deciding what the actual output should be, what matters most, and what needs action.
- Creators, agencies, freelancers, consultants, and marketing teams need structured outputs that reduce interpretation, not another place to copy and paste notes.
The solution
- Design agents around specific repeatable jobs: client request plans, UGC deliverables, revision summaries, weekly client updates, and missing-information checks.
- Make the user-facing experience clarify what the agent does, who it is best for, what inputs it handles, and what useful output it creates.
- Prioritise handoff-ready structure: scope, deliverables, risks, blockers, approvals, dates, missing information, next steps, and action-ready summaries.
Workflow System
A repeatable custom agent design loop for turning inputs into outputs.
The work was not just writing prompts. It was designing the shape of the output first, then building each agent around the real workflow moment it needed to support. This same process can be applied to other service, creator, agency, and operations workflows.
Identify the messy input
Define the source material the agent must handle: briefs, campaign notes, emails, meeting notes, proposals, SOWs, feedback, or review comments.
Choose the output shape
Decide whether the user needs a request plan, delivery checklist, script outline, revision summary, weekly update, or missing-information list.
Structure the agent logic
Build the agent around extraction, organisation, prioritisation, risk detection, blocker detection, missing-information checks, and next-step clarity.
Package the agent for use
Turn the workflow into clear instructions, use-case language, example outputs, gallery assets, and a promise that helps users understand when to use it.
Make it handoff-ready
Keep the final output organised enough that a team member, creator, freelancer, or client-facing operator can act on it without decoding it again.
Product Decisions
The choices that made the agents feel specific instead of generic.
The strongest design decision was to avoid broad “business AI assistant” language. Each agent needed a defined job, a clear user, a recognisable input, and an output people could immediately understand as useful.
01
Messy source material
Briefs, notes, feedback, SOWs, proposals, calls, and messages.
02
Structured extraction
Scope, deliverables, approvals, risks, blockers, dependencies, and missing information.
03
Handoff-ready output
A clear plan, checklist, revision summary, or update the team can use.
Narrow use case first. Flexible AI second.
The agents are built around repeatable operational moments rather than open-ended prompting. That makes the promise easier to understand and the output easier to trust: the user knows what to input, what the agent will organise, what it will flag, and what kind of output they should expect.
Outputs before prompts
The agent value comes from the structure of the result, not from sounding clever. The output spec comes first, then the prompt architecture supports it.
Execution before discovery
The UGC agents are deliberately positioned for downstream execution: turning approved briefs and angles into things people can film, revise, and send.
Team clarity before automation hype
The language stays grounded in real team pain: scattered notes, unclear approvals, missing information, and admin-heavy handoffs.
User-facing clarity matters
The icon, title, description, examples, and preview screens all explain the workflow promise so the agent feels practical before someone uses it.
Live Notion Agents
Live agents for practical business workflows.
These live Notion agents show three custom agent use cases: client request planning, UGC deliverables, and campaign handoff. Each one starts with a real business workflow, defines the input clearly, and turns messy information into an output teams can use immediately.
UGC Playbook Brief-to-Delivery Agent
Built for downstream execution: transforming existing briefs, campaign notes, client inputs, and approved angles into hooks, openings, shot lists, scripts, checklists, and filming-ready deliverables.
Client Request Architect
Turns client briefs, emails, meeting notes, proposals, SOWs, transcripts, or scattered messages into a request plan with scope, deliverables, risks, approvals, dependencies, and missing information.
UGC Brief-to-Delivery Agent
Supports three core workflows: brief-to-deliverables, feedback-to-revision summary, and campaign notes-to-weekly client update for creators, marketing teams, and agencies.
Live Notion agents: These agents are live in the Notion Marketplace and published by The UGC Playbook. They show the end-to-end capability behind a custom agent build: workflow diagnosis, prompt logic, output structure, user-facing explanation, and a practical business use case.
Launch Direction
Positioned for teams who want AI to clean up real workflows.
The agents sit at the intersection of creator operations, agency operations, client service, and business workflow systems. The launch angle is practical: AI should reduce manual sorting, surface what matters, and create outputs teams can use inside the tools they already work in — with the same process adaptable for custom client workflows.
Product category
Notion Marketplace custom AI agents
Primary audience
Creators, agencies, freelancers, consultants, marketers, founders, and project operators
Core loop
Input → extract → organise → clarify → hand off
Visibility channel
Notion Marketplace, creator profile, LinkedIn, workflow education, and custom agent build enquiries
Key Takeaways
What this project clarified.
This project clarified how to design AI agents people actually understand: start with the workflow, define the output, then build the agent around a repeatable business use case.
Specificity builds trust
A narrow agent with a clear output is easier to understand — and easier to sell — than a broad AI assistant that promises everything.
Workflow design is product design
The value is not only in the AI response. It is in defining the repeatable path from messy input to useful output, then making that path easy to trust.
Clarity creates conversion
The user-facing promise has to make the business use case obvious: what to paste in, what the agent returns, and how the output helps the team move faster.
Custom AI Agent Builds
Custom AI agents for messy, repeatable workflows.
If your team repeatedly turns the same kind of notes, briefs, emails, feedback, or client requests into the same kind of output, that workflow can usually become a focused AI agent. I design the workflow map, input rules, output structure, prompt logic, guardrails, test cases, and user-facing copy so the agent feels practical, repeatable, and easy to use.
Best fit
Agencies, creators, consultants, founders, marketers, and client-facing teams
Build focus
Custom AI agents for briefs, client requests, revisions, updates, and operations
What I define
Workflow map, input rules, output structure, prompt logic, guardrails, test cases, and user-facing copy
Outcome
A practical agent that turns scattered information into clear next steps, summaries, checklists, or handoff documents