Company project
Multi-Agent Research Assistant
An AI research system where specialised agents work together to plan research, collect information, analyse results, verify findings and generate structured reports.
- Planner
- Researcher
- Analyst
- Fact Checker
- Writer
- Structured reports
The problem
Good research is slow: a question has to be broken down, sources gathered, findings compared and checked, and everything written up clearly.
Asking a single AI prompt to do all of this at once tends to produce answers that sound confident but are shallow and hard to verify.
Our solution
We built a research system in which specialised agents each handle one stage of the work and pass structured results to the next.
Splitting the job into roles makes every step more focused — and makes the output easier to inspect, check and improve.
How it works
- 01
Planner
Breaks the research question into sub-questions and a plan of work.
- 02
Researcher
Collects relevant information for each part of the plan.
- 03
Analyst
Compares findings, identifies patterns and highlights gaps.
- 04
Fact Checker
Reviews claims against the collected material and flags anything weakly supported.
- 05
Writer
Turns the verified findings into a clear, structured report.
Architecture & workflow
- Planner
- Researcher
- Analyst
- Fact Checker
- Writer
Features
Role-based agents
Each agent has a narrow, clearly defined responsibility.
Structured hand-offs
Agents exchange defined formats rather than loose text.
Verification stage
Findings are checked before anything is written up.
Readable reports
Output is organised into a consistent report structure.
Inspectable steps
Intermediate results can be reviewed at every stage.
Technology stack
- AI
- Large language model APIsMulti-agent orchestration
- Research
- Search & retrieval tools
- Output
- Structured report generation
Screenshots & demo
Screenshots and a demo walkthrough will be added here.
Want to see it sooner? Ask us for a live demo.
Challenges
Keeping agents focused
Each agent needs narrow, clear instructions; broad prompts make roles blur together and repeat each other's work.
Passing the right context
Hand-offs must carry enough information to be useful without overwhelming the next agent with everything gathered so far.
Verification
Checking that claims are supported by the collected material is what separates a useful report from a convincing-sounding one.
What we learned
- Breaking a complex task into roles makes AI output easier to inspect and improve.
- A dedicated verification stage is worth its cost.
- Defined formats between agents make the whole pipeline more predictable.
Want something similar? Talk to us.
Tell us about the process you want to improve, and we'll help work out what makes sense.
Start a Project