AI Engineering — Case Study
FlowForge — Autonomous Task Decomposition & Execution Engine
A multi-agent pipeline that takes a single project brief and turns it into a finished deliverable — planning, researching, drafting and reviewing itself through role-specialized agents with feedback loops between them.
01 — Overview
One brief in, a finished draft out
FlowForge exists to answer a specific question: can a brief alone — no step-by-step instructions — be turned into a reviewed, structured deliverable by agents that plan and critique their own work?
A single generalist LLM call struggles with multi-stage work: it tends to blend planning, research and writing into one pass, with no real checkpoint for quality. FlowForge instead decomposes the brief into a pipeline of specialized roles, each with a narrow job and a clear handoff to the next, so the system can catch its own mistakes before they reach the final output.
02 — How it works
Role-specialized agents, inter-agent feedback
Planner
Ingests the raw project brief and decomposes it into a task graph — discrete, ordered sub-tasks each subsequent agent can act on independently.
Researcher
Gathers the supporting information each sub-task needs before drafting starts, keeping the writer grounded rather than improvising from the brief alone.
Drafter
Produces the actual deliverable content per sub-task, using the planner's structure and the researcher's material as its working context.
Reviewer
Scores the draft against a quality threshold and either approves it or sends it back with specific feedback — the loop that keeps the pipeline from shipping a first draft.
The reviewer's feedback loop is the core design decision: rather than a single linear pass, drafts cycle back to the drafting stage until they clear a defined quality threshold, at which point the pipeline exits and assembles the final structured document.
03 — Tech Stack
What it's built with
04 — Learnings
Specialization beats a single mega-prompt
Splitting planning, research, drafting and review into distinct agents produced more coherent output than asking one agent to do all four in sequence.
The feedback loop is the product
The reviewer-to-drafter loop, not any individual agent, is what made output quality consistent — the exit condition is what stops the system from either under- or over-iterating.
Structured handoffs prevent context loss
Passing structured intermediate outputs (task graphs, research notes) between agents, rather than raw free text, kept later stages from losing the intent of earlier ones.