Specialist Agents

The EQUIRE AI is not one model with one prompt. It is a set of specialist agents, each focused on a phase of the deal lifecycle, each with its own prompt, tool set, and model tier. Specialists are invoked from the workflow surfaces that need them — intake fires when a deal is created or an email comes in, lease fires when a lease document is processed, and so on. The chat assistant has access to the same expertise via the per-mode tool sets described in Chat modes.

How Specialists Fit In

Every specialist follows the same contract: it returns structured output, it never writes to deal data without your approval, and it is tagged with a feature so its usage is attributable in your AI usage reports. None of the specialists block — they produce a draft, a checklist, or a recommendation, and the deal team decides what to act on.

Where a specialist needs to gather evidence (research and prospect enrichment), it runs a bounded tool loop with a step limit. Where it just analyzes text (intake, lease, legal, diligence, debt, closing), it runs a single structured-output pass.

Document Specialists

These six agents read deal documents and produce structured analysis. They run in the document processing pipeline and are also surfaced through chat tools when relevant.

Intake Agent

Purpose — Screens new deals from offering memorandums, teasers, and broker emails. Produces a mandate-fit score (0–100), property and financial summaries, risk flags, and a recommendation: pass, quick look, or full underwrite.

Inputs — Raw email or document text plus optional PDF / Excel attachments and the fund's investment criteria (target property types, markets, deal size, return ranges, leverage).

Output — A structured intake recommendation with property details, deal economics, sponsor and broker information, mandate-fit drivers and concerns, and missing-data flags.

When it runs — On POST /api/deals/intake when you create a deal, and on inbound email through the intake webhook.

Model — Sonnet (critical tier) by default; configurable to Haiku for high-volume screening. Feature tag: document-processing.

Lease Agent

Purpose — Reads the rent roll and lease abstracts to produce portfolio-level intelligence: WALT, near-term rollover risk, non-standard clauses, options (renewal, expansion, termination), cross-tenant rent and escalation comparison.

Inputs — Lease text (PDFs or extracted) plus optional market context.

Output — Per-tenant abstracts, options inventory, rollover-risk summary, non-standard-terms list with deviation risk, and the top recommendations for the underwriting team.

When it runs — During document processing on lease and rent roll uploads.

Model — Sonnet by default; configurable to Haiku. Feature tag: document-processing.

Legal Agent

Purpose — Reviews purchase and sale agreements. Extracts key clauses across 13 categories (price, earnest money, contingencies, reps and warranties, etc.), maps title exceptions, generates a first-pass issue list and closing checklist, and flags deviations from an institutional playbook.

Inputs — PSA text plus optional title report and survey.

Output — PSA summary, key clauses, title exceptions with impact and recommendations, issue list, closing checklist, and a list of deviations from market.

When it runs — During the diligence and closing phases when PSA documents are uploaded.

Model — Sonnet (critical) by default; configurable to Haiku. Feature tag: document-processing.

Diligence Agent

Purpose — Coordinates diligence. Maintains the DD checklist, tracks document request status, generates escalation-tiered follow-up messages, and supports the Diligence Command Center's phase-gate decision: can the deal advance, what blocks it, and who owns the next action?

Inputs — Deal context, current phase, property type, and the existing DD item state.

Output — Checklist state, Phase Blockers, material risks, outstanding requests, follow-up drafts (with escalation level), findings and red flags, and a phase-readiness assessment.

When it runs — When the Diligence Command Center runs a diligence scan, when phase changes, or when a user explicitly asks for a DD update through chat.

Model — Sonnet (chat tier) by default — DD is orchestration, not deep analysis. Feature tag: document-processing.

Debt Agent

Purpose — Builds the lender package and analyzes financing strategy. Produces a property / financial / tenant / market / sponsor summary, compares term sheets across seven lender types (bank, life co, CMBS, agency, bridge, debt fund, other), runs DSCR and LTV sensitivity, and drafts anticipated lender Q&A.

Inputs — Deal context plus optional target lender types and existing term sheets.

Output — Lender package, term-sheet comparison, sensitivity scenarios (rate stress ±100–300 bps, NOI stress ±5–15%), Q&A by category, and a recommendation with alternatives.

When it runs — During financing and IC approval workflows.

Model — Sonnet by default. Feature tag: document-processing.

Closing Agent

Purpose — Coordinates the closing. Manages the closing checklist with blocking flags, verifies document completeness, reconciles prorations (taxes, insurance, CAM, rents) and final economics, and generates post-close tasks and lessons learned.

Inputs — Deal context plus an optional closing date.

Output — Closing checklist, status summary, document completeness check, prorations with buy- and sell-side credits, final cash-to-close reconciliation, post-close tasks with deadlines, and lessons learned.

When it runs — When transitioning to the closing phase or generating a closing statement.

Model — Sonnet by default. Feature tag: deliverable.

Research Specialists

These two agents run multi-step tool loops to gather evidence rather than analyze a fixed document.

Corbis Research Agent

Purpose — Performs market research via the Corbis MCP server. Pulls market data, academic papers, economic indicators, and web search results to support DCF assumptions, narrative generation, or ad-hoc analyst questions.

Inputs — A research prompt plus a configuration that picks a mode: market, academic, economic, or comprehensive.

Output — A structured research report with title, executive summary, key findings (each tagged with source and confidence), data points (metric, value, context), and the full source list.

Tools — Corbis MCP tools grouped by mode. Runs as a bounded tool loop (up to 12 steps).

When it runs — From the valuation analyst pipeline, from research-driven chat queries, and from origination flows that need market context.

Requirements — CORBIS_MCP_TOKEN must be configured at the platform level. Without it, the agent returns a clear error rather than guessing.

Model — Sonnet (chat tier) by default; configurable to Opus (critical). Feature tag: corbis-research.

Prospect Research Agent

Purpose — Enriches prospect records during sourcing. Gathers corroborating sources via Tavily, attaches them durably to the prospect, and optionally produces structured findings — owner / debt checks, market snapshots, outreach drafts, or red-team challenges to the mandate fit.

Inputs — A prospect search result, a sourcing profile (SearchProfile), the requested action, and the org and user IDs.

Output — A markdown summary, durable source attachments (written to prospect_sources), structured findings with confidence, remaining gaps, an overall confidence score, and recommended next actions.

Tools — Tavily search and a listExistingSources lookup that runs as the first step (so the agent never duplicates a source you already have).

When it runs — On POST /api/sourcing/results/[resultId]/agent-runs during the prospect research workflow.

Model — Haiku (fast) for market snapshots; Sonnet for everything else. Feature tag: origination.

How the Assistant Picks a Specialist

The assistant decides which specialist handles a request based on the surface that originated it, not from a single global router:

• The intake agent is invoked by the intake routes and the inbound email webhook. It does not run from the chat assistant.
• The lease, legal, debt, and closing agents run inside the document processing pipeline when their target document type is uploaded. The chat assistant can summon their analysis through tool calls but does not re-run them on every message.
• The diligence agent runs from the diligence dashboard and is also reachable through deal-mode chat tools.
• The Corbis research agent runs from valuation analyst steps, IC memo generation, and on-demand research questions.
• The prospect research agent runs from the sourcing workflow when you request enrichment on a search result.

There is also an internal agent registry and routing helper that scores user intent against specialist keywords, deal phase, and capabilities. It is plumbing for future chat routing rather than a user-facing feature today — when a chat message could be answered by any specialist's tools, the chat backend exposes those tools directly rather than handing off to a separate agent process.

Where to Go Next

• For the full per-mode tool catalog the chat assistant exposes, see Tool reference.
• For which model handles which feature tag and what attribution looks like, see Models and routing.
• For provenance, hallucination handling, and human-in-the-loop checkpoints across all specialists, see Trust and safety.