Building Deep Research the boring way first Update
Oroimen has been growing a new capability: Deep Research.
But instead of jumping straight into “let an AI search the web repeatedly until it feels done”, I wanted to build the boring parts first — the parts that make the eventual research system measurable, recoverable and trustworthy.
Over the last few milestones, Oroimen gained the foundations for an iterative research loop:
research question → plan → search → evidence → identify what is still missing → search again → stop
The important bit is that the AI doesn't get unlimited control of that loop. Models can propose what to search for next, but deterministic code controls budgets, duplicate searches, cancellation, recovery and when another research wave is actually allowed.
It can now checkpoint its progress, recover safely after interruption, keep provenance between searches and sources, and distinguish between “I found another URL” and “I actually gained new evidence”.
This week I also stopped adding features for a moment and split that research loop into clearer modules: state, evidence/provenance, assessment, orchestration and persistence.
Why bother?
Because the next stage is the part I'm most excited about: experimentation.
Instead of guessing what makes a great Deep Research system, I want Oroimen to have a frozen benchmark where different strategies can compete against the same baseline.
For example:
Does reflecting on evidence after every search wave actually help? Does explicit gap detection improve coverage? When does another search stop adding useful information? Does source diversification matter more than a smarter planner? Does claim verification improve the final answer enough to justify its cost?
There are already excellent Deep Research products from much larger teams. I'm not trying to pretend I can out-invent all of them from scratch.
The approach here is simpler:
study what seems to work, turn those ideas into hypotheses, measure them, and keep only what demonstrably improves Oroimen.
And throughout that process, the original principle stays the same:
Your AI can change. Your memory shouldn't have to.
Next up: build the evaluation laboratory, measure the first real quality baseline, and then start breaking things experimentally.
Log in or sign up for Devpost to join the conversation.