What Is an AI Agent? A Practical Guide for Asset Finance and Leasing Leaders
If you’ve spent time in AI or tech circles recently, you’ve likely come across the term AI agent. It’s more than just a trending phrase, it signals a shift in how work is done.
The business landscape is entering a new era, driven not just by artificial intelligence (AI), but by agentic AI autonomous, goal-driven software agents capable of carrying out complex, multi-step tasks across systems with little human intervention.
Far beyond chatbots or copilots, AI agents represent a step change. They’re not just conversational; they plan, act, and improve as they go. This shift is being accelerated by breakthroughs in large language models (LLMs) and orchestration frameworks that let agents reason, interact with APIs, and manage tasks end-to-end.
Enterprise leaders are moving fast. Salesforce, Oracle, Microsoft, IBM, and Google Cloud are all embedding AI agents into their platforms. And forward-thinking businesses, including those in leasing, asset finance, and digital operations are exploring how agents might reshape their workflows.
But with the hype comes confusion. What exactly is an AI agent? How is it different from a chatbot or search engine? And why is this so relevant to asset finance?
What Is an AI Agent?
An AI agent is a software-based system capable of interpreting goals, making decisions, and autonomously executing tasks, often across multiple applications or systems.
Rather than acting as a passive assistant, an agent behaves more like a digital team member, operating independently and learning over time.
As Oracle explains, “AI agents examine their environments, take actions as prescribed by their roles, and adjust based on their experiences.”
Most agents combine:
- Natural language capabilities (via LLMs like GPT or Gemini)
- Task planning and logic engines
- Contextual memory to manage state and history
- Integration with tools and systems such as CRMs, ERPs, and APIs
For example, in the asset finance, lending and leasing industry, an AI agent might process:
- Credit application
- Commercial loan document verification
- Update back end financials systems
- Handle loan or leases objections, issues, questions immediately and follow up on partial loan application unsubmitted
How AI Agents Work
AI agents function through a blend of AI technologies and decision-making frameworks. Their core components often include:
- Natural Language Understanding: Agents use LLMs like GPT or Gemini to comprehend instructions phrased in everyday language.
- Planning Module: Translates user intent into a series of actionable steps.
- Memory and Context Management: Keeps track of what’s already happened in a task, so they don’t restart from scratch.
- Tool Use Capabilities: Allows the agent to connect with business software (e.g., Salesforce, Odoo, Oracle).
- Feedback Loops: Enables them to improve performance by learning from outcomes, both successes and failures.
- This layered design means AI agents are capable of handling complex, multi-step processes, including those that span departments or rely on multiple data sources.
This enables agents to handle workflows that would typically require multiple people or departments, agents go beyond chatbots “by making decisions and executing tasks on the user’s behalf.”
Not Just Smarter Chatbots: The Key Differences
It’s easy to confuse AI agents with tools we already use. Here’s how they differ:
1. Chatbots: Pre-scripted, reactive, and limited
Chatbots, whether on a bank’s website or in a customer service portal, follow predefined rules. You ask a question; they retrieve an answer. Some are more advanced, using NLP (natural language processing), but most can’t perform actions beyond delivering scripted replies.
They’re not truly intelligent, and certainly not autonomous.
2. Search engines: Great at retrieval, not execution
Search tools like Google (or AI-powered ones like Bing Copilot) help users find information. But they don’t act on it. They’re query-response systems: you ask; they show results. They’re useful, but they don’t move anything forward unless you do something next.
3. AI Agents: Autonomous, goal-driven, multi-step
Agents don’t just reply, they take next actions. They can read an email, understand what’s being asked, access a customer database, retrieve a contract, draft a response, and send it. They can manage multi-step workflows, make decisions based on past data, and even collaborate with other agents to achieve complex objectives. Microsoft refers to this as the ability to “work alongside humans or other agents to complete tasks or make decisions on your behalf.”
Why Now? The Tech Stack Making It Possible
Four innovations are behind the rise of agentic AI:
1. Large Language Models (LLMs)
AI agents are built on the same types of models that power ChatGPT and Gemini. These LLMs have become far better at understanding human language, interpreting intent, summarizing data, and maintaining context over long interactions.
This contextual understanding is key to agent behaviour.
2. Orchestration frameworks
Tools like LangChain, AutoGen, CrewAI, and ReAct enable agents to go beyond generating text. They can now interact with APIs, call functions, access databases, and chain multiple steps together to reach an outcome.
3. Memory and Retrieval-Augmented Generation (RAG)
Unlike a one-off chatbot session, agents retain memory, allowing them to understand longer-term goals. They can reference internal documentation, past interactions, or historical data using RAG architectures connecting model output with reliable knowledge bases.
4. Tool use and autonomy layers
Agentic systems can be instructed to choose from various tools to get the job done. For example, a lending agent might choose to use an OCR tool to extract data from a scanned form, a scoring API to assess creditworthiness, and then a CRM integration to update the borrower’s profile.
Agents can not only generate outputs but also take actions, make decisions, and even delegate tasks to other agents. This allows them to operate much like skilled knowledge workers just faster and often more accurately
Why It Matters for Business and Especially for the Asset Finance Industry
In industries like asset finance and leasing, where processes are often data heavy and rule bound, AI agents offer enormous potential. For example:
- Underwriting automation: An AI agent could assess risk factors, retrieve credit histories, flag anomalies, and even recommend approval tiers.
- Onboarding coordination: Once a deal is closed, an agent could coordinate setup with operations, update systems of record, and trigger compliance workflows.
- Customer service: Agents could resolve routine inquiries, flag complex cases for escalation, and follow up with customers automatically.
BCG reports that early pilots of AI agents in enterprise settings have shown 20–30% efficiency improvements in targeted workflows, especially where processes span multiple systems.
Are Agents Ready?
Good question, and an important one. Despite the promise, there are challenges. Most notably:
- Context management: Agents must keep track of goals, steps, and outcomes over time something LLMs don’t naturally excel at without good architecture.
- Trust and oversight: In regulated environments, autonomous decisions must be explainable and auditable.
- Integration complexity: Not all systems are easily “agent-ready.” A thoughtful integration strategy is key.
As IBM notes successful implementations depend on governance “feedback loops, rules-based interventions, and escalation protocols” that ensure agents act in line with policy and business logic.
Getting Started: Strategy Before Technology
AI agents aren’t futuristic anymore. They’re already reshaping how businesses operate, from customer service to decision support and operational execution.
Our Agentification Readiness Review is a consulting-led discovery and roadmap development for tech leader to:
- Executive alignment workshops with business and IT stakeholders
- Use case and customer journey mapping across lending lifecycle
- Platform and data ecosystem assessment
- AI maturity benchmarking and peer comparison
- Phased implementation roadmap with ROI indicators
- Technology recommendations and capability gap analysis.
Let’s identify your first agent opportunity, together.
