Deep Research Agent

The Deep Research Agent is a Python decision-support prototype that uses Gemini 2.5 Pro to move from an initial question to staged research, evidence assessment, and a source-attributed report. Its central idea is that useful research should expose its reasoning path rather than hide everything behind one answer.

Problem and intended users

A single prompt can produce a fluent response before the system understands the decision, its constraints, or the standard of evidence required. This project is intended for researchers, analysts, builders, and decision-makers who need a more deliberate workflow and a recoverable research session.

My role and contribution

I created and developed the system. I designed the six-stage flow, conversation modes, session handling, evidence and confidence treatment, report formats, and the Python implementation.

Approach and technology

The application moves through query collection, adaptive context gathering, research, evidence assessment, analysis, and report generation. Different conversation modes change the depth of clarification, while session persistence allows work to be resumed.

Reports can be quick, standard, or detailed. The generated output includes source attribution, confidence indicators, implementation guidance, and risk considerations so a reader can inspect more than the final recommendation.

Synthetic output example

The following fictional example illustrates the report structure rather than a real research result or verified recommendation:

SYNTHETIC DEMONSTRATION
Question: Which support workflow should a small product team investigate first?
Evidence register: Source A — direct documentation; Source B — dated field note
Assessment: Documentation is current; the field note needs independent confirmation
Recommendation: Run a reversible pilot before changing the operating process
Open risk: The example contains no production data or measured outcome

Evidence and current outcome

The public source repository documents and implements the staged workflow, Gemini integration, session management, and report generation. It is a complete working project, but I do not publish independent evaluations of factual accuracy, research completeness, or decision quality.

Limitations and status

The project is a research and decision-support system, not an autonomous authority. Its output depends on model behaviour and available sources, and important conclusions still need source checking and human evaluation. API access and provider credentials are required to run it.