Cookbooks · Investment & Private Equity · Mid-market screening
The research agents agreed it was attractive. They were evaluating different businesses.
18 October 2026 · FinAnalytic Editorial · 4 min read
Reference implementation. An illustrative business scenario showing how FinAnalytic would approach the problem. It does not describe a completed client deployment or measured results.
The market agent likes the sector. The financial agent likes the growth. The risk agent has produced a short list of concerns. During committee preparation, the analyst discovers that one used group revenue, another assessed a subsidiary and the third included an acquisition that has not closed.
More research created another reconciliation task
In this illustrative mid-market screening exercise, separate agents have produced competent-looking briefs against different company boundaries and dates. A long report has concealed an unresolved question: what, exactly, is the opportunity being screened?
A generic framework can support a better design. The limitation here is the application’s coordination: task completion was rewarded before the team established a common mandate, evidence perimeter and decision boundary.
Resolve the mandate before commissioning research
Our Forward Deployed Engineering approach would start with investment professionals and analysts reviewing the actual screening decision. Which criteria are exclusions? Which are preferences? What needs evidence before the opportunity can proceed, and what belongs in later diligence?
The business requirement resolver would make ambiguous terms explicit. “Recurring revenue” might refer to a contractual commitment, repeated orders or a management classification; those are different claims. A domain ontology could link company entities, reporting periods, metrics, documents and mandate criteria without flattening them into a single narrative.
Give specialists one question and one evidence perimeter
For an investigation requiring several workstreams, a Deep Agents-based harness could support controlled delegation and retained progress. The broader application would establish which company and period are in scope, attach sources to findings and distinguish unresolved questions from accepted facts.
The coordinator should return contradictions to the relevant workstream, rather than average several confident answers. A missing document should remain a request. If new information changes the company perimeter, affected conclusions need reconsideration. Choosing a harness is a starting point for testing these behaviours, not proof they have been achieved.
The output is a first-review brief, not an investment verdict
The proposed brief would separate mandate fit, evidence supporting it, material uncertainties and follow-up questions. It would identify the next accountable analyst or investment professional. Calculations would be independently checked, and access would follow the deal team’s permissions.
A screening assistant would not authorise an investment, establish valuation fairness or substitute for financial, legal and commercial diligence. Its useful boundary is helping people decide which questions merit the next round of work.
Measure whether the team reaches a better next step
Assess time to an accepted initial brief, unsupported claims, missed mandate exclusions, reviewer corrections and duplicated investigation. Compare similar opportunities and include difficult cases where the appropriate outcome is “insufficient evidence”.
A faster screening process does not demonstrate better returns. Business value would first be evidenced through decision preparation and analyst capacity, net of checking and system costs. The intended improvement is a more coherent use of the investment team’s attention, with the remaining uncertainty visible.
Bring one difficult handoff into focus
FinAnalytic’s Applied AI Solutions work begins with the people, process and evidence behind a business problem. For investment & private equity · mid-market screening, a useful starting point is one recurring exception, an accountable business owner and a measurable baseline.
Research basis & further reading
- LangChain: Deep Agents capabilities and execution model
- Anthropic: effective harnesses for long-running agents
- W3C: ontology concepts and relationships (OWL 2 Primer)
Sources checked 18 October 2026. The operating scenario and proposed design are FinAnalytic’s illustrative interpretation; the references do not validate a FinAnalytic deployment or its results. Cover illustration generated for this article.