Agentic AI is software that receives a goal rather than a single instruction, then plans a sequence of steps, calls tools, and acts across those steps to reach the goal with limited human input. In commercial real estate, an agentic system can screen a deal end to end: pull the offering memorandum, extract rents, run the underwriting, and flag the result.
How Does Agentic AI Work?
Agentic AI works by wrapping a language model in a loop that plans, acts, observes, and repeats until a goal is met. The model decides which tool to call next, such as a document parser or a financial model, reads the result, and chooses the following step. State carries across steps, so the system remembers what it has already done.
The distinction from a single prompt is autonomy over multiple steps. Agentic AI refers to systems that receive a goal specification rather than a task specification, select and invoke tools to pursue that goal, and maintain state across multiple inference steps, per definitions summarized by MIT Sloan and Moveworks. Gartner projects that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025.
Component | Function |
Goal | The outcome the system is asked to reach, not a single task |
Planner | Model that chooses the next step toward the goal |
Tools | External functions the agent can call, such as an extractor |
Memory | State carried across steps so context persists |
Why Agentic AI Matters
Agentic AI matters because CRE workflows are multi-step, and a system that only answers one question at a time still leaves the analyst to chain the steps. Deal screening requires reading a document, extracting fields, running a model, and comparing against a buy box. An agentic system chains those steps itself, turning a sequence of prompts into one delegated job.
Autonomy raises the stakes on oversight. Gartner projects that by 2028, 15% of day-to-day work decisions will be made autonomously through agentic AI, up from none in 2024. When a system acts across steps without a human at each one, an early error compounds through the chain, which is why a human-in-the-loop checkpoint at the decision boundary is the standard control rather than an optional one.
Example
Agentic AI is clearest in a deal-screening run measured against manual effort. An analyst screening one deal by hand reads the offering memorandum, abstracts leases, and builds the underwriting, a process a single 30-to-50 page lease alone can take 3 to 8 hours to abstract, per industry estimates from Lextract and Kolena.
Step | Agent action | Tool called |
1 | Read the offering memorandum | Document parser |
2 | Extract rents and lease terms | Field extractor |
3 | Compute NOI and cap rate | Financial model |
4 | Compare against the buy box | Rules check |
5 | Flag pass or fail for review | Report writer |
An agent runs steps 1 through 5 without a prompt at each stage, then hands a completed screening memo to the analyst. If manual abstraction of the deal's leases alone runs 8 hours, and the agent produces a first-pass memo in minutes for human review, the analyst's time shifts from producing the analysis to checking it. The 5 steps become one delegated job with one review point.
Variations and Edge Cases
Agentic AI varies by how much autonomy it holds and how many agents cooperate. The variant chosen sets how far the system runs before a human intervenes.
Variant | Behavior |
Single-agent | One agent plans and acts toward a goal |
Multi-agent | Several specialized agents divide the work |
Supervised agent | Pauses for approval at defined checkpoints |
Fully autonomous | Runs the full chain without a human step |
Tool-limited | Restricted to a fixed set of safe tools |
Agentic AI vs AI Copilot
Agentic AI is often confused with an AI copilot, but the difference is who drives. An AI copilot assists a person inside their workflow, suggesting and completing tasks the human requests one at a time. Agentic AI takes a goal and runs the workflow itself, chaining steps and calling tools with the human reviewing the outcome rather than each step.
A copilot waits for the next instruction; an agent decides the next instruction. In screening, a copilot answers "what is the base rent in this lease" when asked. An agent is told "screen this deal" and reads the lease, runs the model, and returns a verdict. The copilot amplifies a working analyst; the agent replaces the manual chaining of steps.
Frequently Asked Questions
What is agentic AI in commercial real estate?Agentic AI is software that receives a goal, plans a sequence of steps, calls tools, and acts across those steps with limited human input. In commercial real estate it can screen a deal end to end, from reading the offering memorandum to flagging the underwriting result for review.
How is agentic AI different from an AI copilot?An AI copilot assists a person one task at a time within their workflow, while agentic AI takes a goal and runs the multi-step workflow itself, calling tools and maintaining state. The copilot waits for instructions; the agent decides the next step toward the goal.
Is agentic AI safe for underwriting decisions?Agentic AI acts across steps, so an early error can compound, which is why a human-in-the-loop checkpoint at the decision boundary is the standard control. Gartner projects 15% of day-to-day work decisions will be made autonomously by 2028, up from none in 2024, raising the importance of oversight.
Related Terms
Large Language Model
AI Copilot
Deal Screening
Human-in-the-Loop
Retrieval-Augmented Generation