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Commercial real estate intelligence grounded in academic research, real economic data, and the agent systems behind Corbis and EQUIRE.
Agentic Assets Research Team
Corbis Research
March 12, 2026
8 min read read
By March 2026, AI agents were no longer just a product demo category. They were becoming software infrastructure. OpenAI's March 2025 agent tooling announcement had already pointed in that direction: a Responses API, built-in web search, file search, computer use, an Agents SDK, and tracing. Google's Gemini 2.5 announcement emphasized long context, reasoning, coding, and agentic software workflows. The pattern was clear before 2026 arrived: the winning agent stack would be less about a chat window and more about controlled action.
For commercial real estate and finance teams, that distinction matters. A useful agent does not just answer a question. It retrieves documents, checks assumptions, builds a model, cites sources, opens a task, runs a screen, and leaves behind a trail that another professional can review. That is the difference between an assistant and an operating layer.
Real estate investment work is full of tool boundaries: PDFs, spreadsheets, rent rolls, Argus exports, CRM notes, data rooms, loan docs, market reports, and email threads. An agent that cannot cross those boundaries safely is mostly a summarizer. An agent that can cross them with permissions, logging, and source capture becomes a junior analyst, transaction coordinator, or research associate.
The same is true in finance. Portfolio monitoring, compliance review, earnings analysis, and credit memo drafting all require multi-step work across systems. The agent must know which tool it used, what data it saw, and whether the final output follows the evidence. That makes observability a product requirement, not an engineering nicety.
Agent traces will become especially important in high-stakes domains. If an AI system recommends a market, flags a lease risk, or drafts an investment memo, the team needs to know what happened. Which documents were retrieved? Was the source current? Did the agent rely on a public web result, a proprietary database, or a stale upload? Did it skip a required approval step?
This is why production agent platforms are moving toward sandboxes, permissions, and event histories. The point is not only to prevent errors. It is to make errors inspectable. In a regulated or capital-intensive workflow, untraceable automation is a liability.
The best March 2026 use cases are not fully autonomous acquisitions. They are bounded workflows with measurable outputs: generate a first-pass market brief, reconcile rent roll changes, compare broker deck claims to source documents, extract debt terms, build a diligence checklist, or summarize a tranche of leases. These tasks are valuable because they have clear inputs and a reviewable output.
That is also why CRE teams should resist vague agent adoption. A broad mandate to use AI everywhere creates tool sprawl and weak controls. A tighter mandate to automate specific, evidence-heavy workflows creates learning. The durable value is not that an agent can do something once. It is that the organization can repeat the workflow, review the trace, and improve the process without losing control.
In finance and CRE, the model is only one component. The production system also needs retrieval, permissions, tool boundaries, evaluation, logs, and rollback. The OpenHands Software Agent SDK paper makes this point in the software-agent domain: reliable agents need sandboxed execution, lifecycle control, flexible interfaces, and measurable evaluation. Those same primitives map to CRE workflows. A market-research agent should not silently edit an investment memo. It should work in a draft space, record sources, and present changes for review.
This is where many AI pilots fail. They test model fluency but not workflow reliability. A team asks whether the answer sounds good, but not whether the agent used approved sources, followed the right checklist, preserved the evidence, or failed safely. For capital workflows, those controls are the product. The Doc2Agent paper is another useful reminder: tool-using agents improve when tools are generated, tested, and refined against real tasks rather than assumed to work from documentation alone.
The teams that win with agents will not be the teams that ask the broadest prompts. They will be the teams that turn repeated analytical work into reviewed, traceable workflows.
The FactSet and Google partnership is a useful signal: financial agents will be most valuable when they sit close to trusted data, workflows, and reviewable decisions.
Agent adoption is accelerating, but governance is lagging. CRE firms should use that gap as a design constraint, not a reason to avoid automation.
The lesson from software agents is not just coding speed. It is a work pattern: isolated environments, source files, logs, tests, and human review.