AI Agent for Multi-Step Enterprise Research
A research question is rarely one retrieval. This agent breaks it into sub-questions, gathers evidence across your own systems and approved external sources, reports where those sources disagree with each other, and writes up a finding with every claim carrying its origin.
What is an AI research agent?
An AI research agent is a governed software worker that carries out multi-step research. It decomposes a question into sub-questions, gathers evidence across internal systems and approved external sources, compares what those sources say, reports contradictions rather than resolving them silently, and produces a report where every claim is traceable to a typed source.
What it does
What it is not
A confident synthesis of three sources that disagree
Real research questions resolve into six smaller ones, and the sources that answer them rarely agree. What usually gets produced is a fluent summary that quietly picks a side, so the disagreement — which is often the most important finding — disappears into a well-written paragraph nobody can audit.
The question is treated as one lookup
A question needing six sub-answers gets one retrieval, and the parts nobody searched for are answered from general knowledge.
Sources are blended, not compared
Two documents give different figures and the output averages them into a number that appears in neither.
Internal and external get equal weight
A public article and your own system of record are cited side by side as though they carried the same authority.
The trail is not kept
The report reads well and there is no way to establish which source produced which sentence.
Research that shows its disagreements
Decomposition
Break It Into Answerable Parts
Before searching anything.
The question is decomposed into sub-questions that a source could actually answer, with the dependencies between them made explicit, so the research plan is visible and reviewable before any effort is spent gathering evidence.
- Sub-questions stated before retrieval
- Dependencies between them made explicit
- Plan reviewable before work starts
- Unanswerable parts identified early
Visible up front
Comparison
Where The Sources Disagree
Reported, never averaged.
Evidence for each sub-question is gathered from several sources and compared, and where they conflict the agent reports both positions with their origin and recency rather than resolving the difference silently in favour of one.
With both positions
Provenance
Every Claim Traces Back
And the source type is named.
Each statement in the report carries the passage it came from and whether that was a system of record, an internal document, a prior decision or an approved external source — because those do not carry the same weight and a reader needs to know which they are reading.
Source and authority
How the AI Research Agent runs a task
- STEP 01
Plan before searching
The question is broken into sub-questions with their dependencies stated, and the plan is shown before retrieval begins, so a reviewer can redirect the research while it is still cheap to redirect.
Question decompositionPlan generation - STEP 02
Search where the answer lives
Each sub-question is routed to the sources most likely to hold it — a system of record for a fact, a wiki for a decision, ticket history for what actually happened, approved external material for published positions.
Source routingFederated search - STEP 03
Gather with provenance
Evidence is collected as passages rather than as paraphrase, each retaining its document, location and date, because a claim that cannot be pointed back at its sentence is not usable in a report anyone has to defend.
Passage extractionProvenance binding - STEP 04
Compare and contradict
Evidence for each sub-question is set against itself, and where two sources conflict both positions are recorded with their type and recency, leaving the reader to weigh them rather than inheriting a silent choice.
Cross-source comparisonConflict detection - STEP 05
Write with the gaps in it
The report answers each sub-question with its evidence, lists unresolved contradictions in one place, and states plainly which parts of the original question the available sources could not settle.
Report draftingOpen questionsCitation typing
Systems the AI Research Agent connects to
Internal sources
Retrieval and verification
Inputs, outputs and runtime
- Ingests
- Research questionPermitted internal sourcesApproved external sourcesScope and date constraintsPrior research
- Produces
- Sub-question research planFindings with typed citationsContradiction listUnresolved questionsWritten research report
- Triggered by
- Research requestDecision preparationPrecedent check before new work
- Human oversight
- A reviewer approves the plan and the finding
- Models
- Open-weight LLMs you host — Llama, Qwen or Mistral class
- Typical latency
- Under an hour for a scoped question
- Deployment
- On-premise or sovereign cloud with egress control
- Data residency
- The question and internal evidence stay inside
Where the Research Agent pays back
Options Research
Investigate several candidate approaches against stated criteria and report where the evidence supports each.
Internal Precedent Discovery
Establish what the organisation has already decided, built or concluded on a question before new work begins.
Technical Due Diligence
Gather evidence about a technology or supplier across internal experience and published material.
Market And Standards Review
Assemble what approved external sources state on a topic, with each position attributed and dated.
Contradiction Audits
Deliberately look for places where internal documents assert different things about the same subject.
Briefing Preparation
Produce a researched briefing with an explicit list of what could not be established from available sources.
AI Research Agent vs chatbots and SaaS copilots
Fluency is the enemy here: the better a synthesis reads, the less likely anyone is to notice that two of its sources flatly contradicted each other and one of them was quietly dropped.
| Generic chatbot | SaaS copilot | VDF AI | |
|---|---|---|---|
| Question handling | One answer | One retrieval | Decomposed into sub-questions |
| Source breadth | Training data | One system | Internal and approved external |
| Conflicting sources | Picks one silently | Shows both, unranked | Reported with type and date |
| Citation type | None | Link only | Source authority stated |
| Plan visibility | None | None | Shown before retrieval |
| Unanswerable parts | Answered anyway | Omitted | Listed as not established |
| Where research runs | Vendor service | Vendor tenancy | Inside your own network |
Governance and controls
Research output tends to be reused long after the context that produced it is forgotten, which makes the date, the source type and the scope of the question part of the finding rather than metadata around it.
Approved sources only
External reach limited to your allowlist
Permissions inherited
Only material the requester may see
Citations typed
Source authority stated per claim
Contradictions preserved
Conflicts never resolved silently
Scope and date recorded
Findings carry their question scope
No decision authority
Recommendations stay with people
Evidence it leaves behind
What changes after rollout
Who runs the AI Research Agent
Strategy analyst
Gets a researched position with its plan visible and its disagreements listed, which makes the review a conversation about evidence rather than an attempt to work out where a paragraph came from.
Principal engineer
Finds out what the organisation already tried and concluded on a technical question before spending a sprint rediscovering it, including the ticket comments where the real reason was recorded.
Knowledge manager
Receives a standing register of contradictions between internal documents, which is a far more actionable signal about knowledge-base health than any measure of coverage or freshness.
Questions about the AI Research Agent
What is an AI research agent?
It is an agent that conducts multi-step research: decomposing a question into sub-questions, gathering evidence across internal systems and approved external sources, comparing what they say, reporting contradictions, and writing a finding where every claim carries its origin.
How is an AI research agent different from a generic chatbot?
A chatbot answers from one retrieval and its own training. This agent plans the research, gathers from several sources, and tells you where those sources disagree instead of picking one.
Can an AI research agent run on-premise on internal and approved sources data?
Yes. The question itself often reveals a decision in progress, and the internal material gathered to answer it is your own, so the whole process runs inside your perimeter.
What does an AI research agent produce, and in what format?
A research report with the sub-question plan, findings per part, a contradiction list showing both positions, typed citations per claim, and an explicit statement of what could not be established.
Where does an AI research agent fit in a governed AI programme?
It researches and stops short of deciding. Recommendations and choices belong to the people accountable for them, and single-question retrieval is better served by the enterprise search assistant.
How is this different from the AI Enterprise Search Assistant?
Depth of the question. The search assistant answers something that has an answer sitting in a document: where is the policy, what did we decide, who owns this. This agent handles questions that no single source answers, where the work is deciding what would answer it, gathering across systems, and reconciling sources that disagree. Using the research agent for a lookup is slower and produces a report where a sentence would do.
Can it reach the open internet?
Only within an allowlist you configure, and every external claim is labelled as external in the citation. Many deployments run it entirely internally. Where external reach is permitted, collection is limited to publicly accessible pages and the report separates what your own systems establish from what a published source asserts, because those are different kinds of evidence and merging them is how internal facts get quietly overridden.
What does it do when sources contradict each other?
Reports both, with the source type, the date and the passage for each. It will note where one is a system of record and another is a document that may be stale, which is usually the resolution, but it does not silently apply that reasoning. A contradiction between two internal sources is itself a finding worth someone knowing about, and averaging or choosing would hide it.
How does it decide when the research is finished?
When each sub-question is either answered with evidence or recorded as not establishable from the available sources. That second outcome is treated as a legitimate result rather than a failure, which matters because the alternative — continuing until something plausible is produced — is exactly how an unsupported claim enters a report that everything else in is sound.
Does it overlap with the competitive intelligence agent?
They share technique and differ in subject. The competitive intelligence agent tracks a defined competitor set continuously, monitoring for change and maintaining a baseline. This agent answers a specific question once, across whatever sources are relevant, and its subject is anything — a technology choice, an internal precedent, a regulatory position. Continuous competitor monitoring is better served by the specialist.
See the research plan before the report
See the AI Research Agent decompose a question and gather evidence across your systems.