AI Research and Analyst Agents
Agents that take a real research question, decompose it, work through approved sources, and return something with every claim cited and every inference labelled as one.
The appeal of a research agent is obvious and the risk is equally so. A confident, well-structured, professionally formatted document that is wrong in three places is more dangerous than no document at all, because it will be forwarded before anyone checks it. Everything here is designed around making the checking cheap.
An AI research agent takes a defined question, breaks it into sub-questions, gathers evidence from an approved source set, weighs and reconciles what it finds, and produces a structured output in which every factual claim is attributed to a source and every inference is explicitly marked as the agent's own reasoning.
What a research agent must get right
- Source discipline. An approved source set with a stated hierarchy, so a filing outweighs a summary and a summary outweighs a forum post. Sources outside the set are not used silently.
- Claim-level citation. Every factual statement carries its source and, where possible, the exact passage. A bibliography at the end is not citation.
- Finding versus inference. What the sources say and what the agent concluded are visually and structurally separate, because they carry different weight.
- Contradiction surfaced. Where sources disagree, both are shown with dates and provenance rather than one being quietly chosen.
- Explicit gaps. What could not be established, stated plainly, so the reader knows where the analysis stops.
An analyst reading the output should be able to verify any single claim in under a minute. That is the design target, and it is what makes the difference between a tool that saves time and one that creates review work.
How we build research agents
Fix the source set and its hierarchy
We define which sources are permitted, which are authoritative, how recency is weighted and what is excluded outright. Where you have paid subscriptions or internal repositories, those become the primary sources rather than whatever is publicly reachable, which is usually the largest single improvement in output quality.
Decompose deliberately
The question is broken into sub-questions with an explicit plan, visible in the output. This is the same planning discipline described under agentic RAG, and it is what makes the depth controllable: you can see what the agent decided to investigate and, more importantly, what it did not.
Verify before assembling
Each claim is checked against the passage it came from before it enters the document. Unsupported statements are removed or demoted to explicitly labelled inference. This pass costs tokens and time, and it is not optional.
Structure the output for checking
Findings with inline citations, a separate analysis section, a stated confidence per conclusion, and a list of what could not be established. The format is designed for an expert skimming with scepticism, not for an executive who will read only the summary.
Bound the depth
Research agents will keep going indefinitely if allowed to. We set explicit ceilings on sub-questions, sources and total spend per run, with a reported cost per report, so depth is a dial you control rather than a surprise on the invoice.
The reviewer is part of the system
We design for a named analyst who reviews before anything is circulated, and we make that review fast: citations one click away, inferences marked, gaps listed. Removing the reviewer is possible on low-stakes questions and unwise on the ones that justified building the agent.
Where research agents earn their place
| Use | What the agent does | What the human still does |
|---|---|---|
| Market and competitor scans | Gathers, structures and cites current positions and changes | Judges significance and strategic implication |
| Due diligence support | Assembles the evidence base against a checklist | Decides what the evidence means for the deal |
| Regulatory monitoring | Tracks changes across sources and flags what applies | Interprets applicability and decides the response |
| Literature and prior art | Finds, summarises and cross-references | Assesses quality and relevance |
| Internal analysis | Pulls together data and documents across systems | Owns the conclusion and the recommendation |
The pattern is consistent: the agent removes the gathering and the formatting, which is most of the hours, and leaves the judgement, which is the part being paid for.
How the engagement runs
Output quality is judged by your own analysts against work they would have produced themselves.
Question types and source set
Recurring research questions catalogued, approved sources and hierarchy fixed, and the output format designed with the analysts who will use it.
Retrieval and decomposition
Source-specific retrieval tools, planning and sub-question handling, scored on evidence coverage per question type.
Verification and assembly
Claim-level checking, contradiction handling, inference labelling and confidence statements.
Analyst evaluation
Real questions run in parallel with your analysts, outputs compared blind, and failures folded into the evaluation set.
Handover
Depth and cost controls, evaluation set, review workflow and the guidance for adding sources.
What you receive
Research output that survives being checked, and the controls that keep it affordable.
Research agent
Deployed with source-specific retrieval tools and bounded depth, as infrastructure as code.
Source policy
Approved sources, hierarchy, recency weighting and exclusions, applied by the system.
Verification layer
Claim-level checking with unsupported statements removed or labelled as inference.
Output template
Findings with inline citations, separated analysis, confidence statements and explicit gaps.
Evaluation set
Real questions with analyst-agreed expectations, used as a release gate.
Depth and cost controls
Ceilings per run with cost per report reported, and the dial to trade depth against spend.
Is this the right engagement?
Worth being direct. AI Research and Analyst Agents is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- Analysts spend most of their time gathering and formatting rather than judging.
- The same categories of research question recur regularly.
- You have authoritative sources or subscriptions the agent can be pointed at.
- A named reviewer will check output before it circulates.
- Citation and traceability are requirements rather than preferences.
Choose something else if
- Each question is unique and highly specialised, so there is no pattern to encode.
- The value is entirely in judgement rather than in gathering.
- Output would circulate unreviewed to people who would act on it directly.
- The necessary sources cannot be accessed lawfully or under their terms.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
Can an AI research agent replace an analyst?
No, and the ones we build are not designed to. They remove the gathering, cross-referencing and formatting, which is where most of the hours go, and leave the judgement with the analyst. The output is explicitly structured to be checked rather than trusted.
How do you stop it citing sources that do not say what it claims?
Every claim is verified against the passage it came from before it enters the document, and anything unsupported is removed or demoted to labelled inference. That verification pass is the most expensive part of a run and the reason the output is worth reading.
Can it use our paid subscriptions and internal repositories?
Yes, where the terms permit programmatic access, and doing so is usually the single largest improvement in quality. We check licence conditions before integrating a source rather than after.
How long does a research run take?
Minutes to tens of minutes depending on the depth ceiling you set, because the agent works through sub-questions and verifies as it goes. Depth, sources and spend are all bounded per run and reported, so a deeper report is a decision rather than a surprise.
How do you know the output is good?
We run real questions in parallel with your analysts during the build and compare the outputs blind. Their assessment sets the bar and their disagreements become the evaluation set, which then gates every subsequent change.
Often paired with this
Most clients combine two or three engagements from the AI Agents & Agentic Automation pillar. These are the ones that most often run immediately before or after.
Agentic RAG
Retrieval that plans, decomposes and verifies for questions single-shot RAG cannot answer, with the cost stated.
Read more →Browser and Computer-Use Automation Agents
Agents that operate a browser where no API exists, with isolated credentials, injection defence and honest limits.
Read more →Custom AI Agent Development
An agent that completes work in your systems, with scoped tools, trajectory evaluation and a circuit breaker.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.