Nova R&D Agent: An Autonomous Research & Intelligence Platform
Designing and building an AI agent capable of autonomously conducting product research, competitive analysis, and market intelligence at scale.
The Challenge
Research-intensive businesses face a compounding problem: the volume of market data, competitor activity, product developments, and industry intelligence grows faster than any team can manually monitor and synthesise.
The client needed an autonomous R&D agent — a system capable of independently conducting structured research tasks: scanning sources, extracting relevant signals, cross-referencing findings, generating research briefs, and flagging emerging trends without human-in-the-loop intervention for routine workflows.
The challenge was not just building AI capabilities but building a trustworthy research system — one whose outputs were accurate, structured, and actionable enough for business decision-making rather than requiring significant human cleanup.
Our Approach
1Research Methodology Design
Before any technical work, we designed the research methodology the agent would follow — defining task types (competitive scan, market trend analysis, product audit, technology research), output formats, quality criteria, and validation checkpoints. The agent's logic was grounded in how a senior researcher would approach each task type.
2Tool & Source Architecture
Identified and integrated the optimal data sources for each research task type: web search APIs, academic databases, patent databases, product repositories, news feeds, and social signals. Built a source credibility scoring system to weight outputs appropriately.
3Agent Workflow Design
Designed structured agent workflows for each research task type — defining the sequence of tool calls, intermediate validation steps, synthesis logic, and output templating. Each workflow was designed to be auditable and reproducible.
4LLM Selection & Prompting
Evaluated multiple LLM configurations for research-quality output. Developed a structured prompt engineering framework optimised for factual accuracy, citation handling, and structured output generation in research contexts.
5Platform Development
Built the full Nova R&D platform: agent orchestration engine, research task queue, output storage and versioning, a structured brief generator, and a dashboard for reviewing, exporting, and acting on research outputs.
Execution
Nova was built with research quality as the primary design constraint — not speed, not feature count. Every architectural decision was evaluated against the question: does this produce more reliable, more accurate research outputs?
The agent uses a structured "research protocol" for each task type — a defined sequence of steps that mirrors how a skilled human researcher would approach the same task. This made agent behaviour predictable, auditable, and improvable.
A human-review checkpoint was built into complex research tasks (not as a crutch, but as a quality gate) — allowing the platform to operate autonomously for routine tasks while flagging edge cases for human oversight.
Results
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