What Real Estate Professionals Need To Know About Agentic Ai

It seems like an oversimplification, but many real estate agents don’t realize they’re using AI, or even interacting with AI — that’s in large part because agentic AI is becoming more commonplace.

AI has already had a meaningful impact on the day-to-day lives of real estate agents, even if they don’t realize it — whether it’s the use of major graphic-design platforms like Canva, social media apps, or embedded AI tools within search engines. Some CRM providers have already rolled out AI-enhanced platforms, and even Zillow has incorporated AI directly into its systems.

But many real estate professionals, despite being steeped in it, have questions about agentic AI — starting with what, exactly, it is.

What is agentic AI?

Agentic AI refers to AI systems with the capacity to independently analyze, make decisions and act based on the information and data prompted and provided to it (in some cases, that could be the internet at large). Agentic AI proceeds with a specific goal, adapts to changing circumstances and endeavors to act beyond automation or simplistic “if-then” logic.

If you’ve ever used platforms such as Microsoft Copilot Studio or OpenAI Operator, you’ve already gotten a taste for an Agentic AI system.

For a real estate agent, this means going beyond the simple task of having AI write them a listing description, pull up market analysis stats, or summarize a report. Instead, agentic AI models could optimize their daily objectives by helping to scale their daily admin tasks, for example, and even engage with clients.

Looking through the lens a bit more at how agents can implement agentic AI into their business, they should first understand the four key elements of agentic AI: Machine learning, automation, natural language processing, and CRM integration. 

Machine learning and advanced analytics

Machine learning helps agentic AI’s predictive endeavors for real estate, such as determining property values, market trends (whether the data indicates a buyer’s or seller’s market, etc.) , and buyer/seller behaviors.

AI agents can interact with website visitors, capture lead information, respond to inquiries immediately, and evaluate buyer intent using conversational follow-up questions. It qualifies leads by updating CRM tags and prioritizing those most likely to convert, ensuring every promising client receives attention while automating the nurturing of long-term prospects.

Workflow automation

This helps with scheduling, task delegation, marketing and goal determination by examining past sales, neighborhood trends and individual preferences. These agentic Al systems can present buyers with listings specifically matching their budget (In NYC, some systems have paused this last feature in response to the 2025 FARE Act), location, and lifestyle criteria, sometimes as soon as a suitable property hits the market.

The AI agent can use this enhanced data to further personalize advice on pricing, staging, and even listing time suggestions, all based on real-time analytics. A real estate agent’s Al assistant can book appointments, coordinate showings based on client/agent availability, and send reminders automatically. A marketing experience can be created that’s hyper-personalized (making sure not to border on being creepy) with targeted emails, SMS, and social media, often with engagement rate analysis.

Natural language processing (NLP)

Natural language processing helps optimize the communication between the agent’s agentic model and the consumer for better outreach.

CRM integration

CRM integration is critical because it helps optimize documentation for follow-up and due diligence, and enables the agent to learn more from every transaction. In commercial and investment real estate, AI agents can plan preventive maintenance such as planning proactive property upkeep on vital operational systems like plumbing, HVAC, pest control, or even elevators if applicable to avoid the higher cost of remediation after a breakdown.

It can also monitor property cap rate performance metrics, recommend portfolio adjustments, and identify changes in rental or property values before they become visible to the broader market.

Real-world implementations for AI agents

What are some real-world, practical implementations? Consider the “APIM” acronym (relating to the use of Agentic AI for real estate professionals): Automate, Personalize, Integrate and Monitor.

Automation

An agent can begin by automating repetitive, time-consuming tasks, such as appointment scheduling, document management, initial CMA, comparable market analysis, price suggestions, and client follow-up. So, you could let the AI model run recurring analysis on the back end, which gives you automatic alerts. Not only does this immediately free up more time for client-facing work, it provides a foundation for more complex Al deployments.

Personalize

Vet and ultimately choose CRM or marketing tools with built-in Al, which can track client preferences and behaviors. This allows you to deliver property suggestions and marketing campaigns that truly resonate with your client relationship or audience. In theory, this would lead to higher engagement, more showings, and quicker closings.

Integrate

Look for agentic Al solutions that plug directly into your CRM, MLS, calendar, and communications platforms. Seamless integration ensures data accuracy, fast adoption, and minimal disruptions to existing processes. Consider deploying agentic Al-powered chatbots to handle inquiries and keep leads.

Maintain a human touch by setting the Al assistant to forward complex questions or reach-outs from top clients to yourself, ensuring your expertise always adds value to the transaction.

Monitor

Always remember that the client hired and trusted you, the human agent. Not an AI model.

Audit your systems just like you’d audit your own process. Regularly review how the agentic Al model (or vendors you’ve hired) is performing in your practice. Fine-tune workflows, provide feedback to your vendors and Al providers, and encourage learning from every client interaction for continuous improvement.

What it all means for agents and consumers

Agentic AI, and its use by real estate professionals, is reshaping the industry by making agents faster, better informed, affording them smarter decision-making, and more client-focused through improved business scalability. Its potential goes beyond routine automation, enabling new business models such as concierge services at scale, dynamic pricing, and continuously optimized marketing.

The advent of any new technology also has potential drawbacks. A few to be conscious about are the need for the agents to make sure the programs they use have proper disclosures and offer consumers transparency on how their information is used.

Real estate professionals should stay informed about the latest developments, thoughtfully integrate, and monitor these powerful tools.

Jules Garcia is an real estate professional with Coldwell Banker Warburg.

This column does not necessarily reflect the opinion of HousingWire’s editorial department and its owners.

To contact the editor responsible for this piece: tracey@hwmedia.com