Addis.

The problem

Turning a vague AI initiative into a buildable agent design is hard for product managers. They open a blank doc, move to a whiteboard that only captures boxes and arrows, paste prompts into an LLM chat, and lose context between tools — ending up with workflow logic, evals, assumptions, and safeguards fragmented across whiteboards, docs, and chat windows.

Even when a workflow is drafted, decomposition is often too vague or unevenly broken down, and teams don't know where to place reflection, what "good" looks like for evaluation, or where safeguards are needed. Before engineering can start, the PM still has to turn messy material into a coherent, buildable, evaluable artifact.

The solution

ADES (Agent Design Studio) helps PMs and AI builders decompose agent workflows before engineering starts. A PM fills in a lightweight Blueprint — initiative, target user, context, desired outcome, constraints, human involvement, and risk level — and ADES generates a structured, editable design artifact rendered as a board of cards.

Crucially, ADES treats reflection logic, eval design, safeguards, and assumptions as first-class parts of the design itself, not afterthoughts. It replaces the fragmented ChatGPT-plus-Miro-plus-docs stack with a single structured artifact that product, design, and engineering can review, edit, and hand off — focused on the pre-build phase most tools skip.

How it works

ADES sends the Blueprint fields and a detailed master prompt to the model, which returns board-ready JSON rendered into an editable canvas. The master prompt casts the model as an expert agent-design strategist optimizing six dimensions — workflow clarity, decomposition quality, reflection logic, eval coverage, safeguard coverage, and handoff readiness — with rules to avoid placeholder steps, add reflection only where uncertainty or risk justifies it, include at least one end-to-end eval, and scale safeguards to risk.

The model is gpt-5 nano as a cost-efficient baseline (moving to gpt-5 mini only if quality gaps persist), integrated via the OpenAI API with Vercel hosting and Firebase for auth and storage; no RAG in v1. A 15-case eval suite spanning typical, edge, and adversarial cases uses model graders and scored well — workflow_clarity 4.73/5, eval_quality 4.64/5, no_placeholder_steps 100% — while surfacing weaker areas in risk calibration (80%), assumption handling (80%), and truncated-JSON completeness, each addressed in prompt iterations.

Who it's for

ADES is for product managers and AI product leads, especially teams designing agents, with adjacent users among AI builders, founders, and technical generalists shaping agent workflows.

The model is B2B, currently free in an early validation stage: anyone can try an interactive demo, and a signed-in user gets one free project generation before a validation prompt tests willingness to pay. The intended long-term revenue model is B2B SaaS subscription — priced per team, workspace, or seat — with the likely buyers being Heads of Product, AI product leads, and founders building agent-driven products.

Why it matters

The enterprise agentic AI market is projected to grow from $2.58B (2024) to $24.50B by 2030, a 46.2% CAGR, and McKinsey reports 62% of organizations are at least experimenting with AI agents — but most remain early and lack mature practices for agent design, evaluation, and governance.

Just as PMs long relied on tools like Jira, structured pre-build design tooling for AI work is becoming strategically important. ADES's bet is a single build-ready artifact that brings workflow, evals, reflection, and safeguards into one place. As an early-stage 0-to-1 prototype, it is scoped to prove the core generation loop before layering on collaboration, deeper analytics, and premium governance features.

At a glance

Project

Addis

Built by

MARIO SORGENTE

One-liner

Agent design studio that helps PMs and AI builders decompose agent workflows before engineering, including steps, safeguards, reflection loops, master prompts, and evaluation graders.