ai · 1 min read
Research Agents: Building Reliable Knowledge Workers
Research agents that gather, synthesise and cite. Here is the architecture Bhogar AI uses to deliver research outputs analysts actually trust.
BABhogar AI TeamProduct & Engineering
Research is the highest-use workload for agentic AI: hours of analyst time replaced by minutes of LLM time, when the agent is built right. The difference between trusted output and slop comes down to citations and recency.
Why it matters
A research agent that returns a confident answer with no sources is worse than nothing. Production agents must cite primary sources, surface dissenting evidence, and disclose what they could not verify.
How Bhogar AI approaches it
Bhogar AI research agents combine a planning loop, a multi-tool retrieval surface (web, knowledge base, internal databases), and a verifier that checks every claim against its source. Outputs are always rendered with inline citations and a confidence score per claim.
- Planning loop with topic decomposition and gap detection
- Multi-source retrieval: web, internal KB, databases, APIs
- Per-claim verification with explicit citation metadata
- Confidence scoring and "could not verify" disclosure
- Outputs export to Markdown, Notion, Confluence and PDF
What you get
Strategy and consulting teams deliver first-draft research briefs in 8 minutes that previously took 4-6 analyst hours, with citation accuracy verified by sampling.