The different types of agents in AI and how they can support collections customers

Artificial Intelligence (AI) has become a game-changer for the collections industry, enabling faster resolutions, better customer experiences, and improved operational performance. But not all AI is created equal. At the heart of many AI systems are agents, which are autonomous systems designed to perceive their environment and act to achieve specific goals.

Understanding the different types of AI agents and how they operate is essential for building trust, ensuring compliance, and optimizing collections strategies. In collections, the choice of agent can directly impact everything from real-time engagement to long-term risk exposure. Let’s explore the major types of agentic AI and how they can (or can’t) support collections customers.

1. Reactive agents

Reactive agents respond directly to stimuli from their environment with predefined rules or behaviors. They don’t have memory or internal models, but simply react based on the current input.

In collections, they’re ideal for real-time decisioning and automated workflows. Reactive agents can trigger compliant, consistent responses to customer behavior. This includes sending a message when a missed payment is detected or offering a payment plan when a customer shows signs of hardship.

Why they work:

• Low risk of non-compliance (no self-learning)
• Fast, rule-based responses support scalability
• Excellent for self-curing strategies and preferred-channel engagement
• Easy to configure and test within cloud-native platforms

Best used for:

• Early-stage collections
• Handling predictable customer interactions
• Automating outreach across channels (SMS, email, push)

2. Deliberative agents

Deliberative agents maintain a model of the world and plan ahead before acting. They analyze different paths and select the one most aligned with their goals. Simply put, they’re a subset of the most advanced AI agents out there, able to engage in strong decision-making roles.

For collections, these agents can potentially be helpful in more complex decision-making scenarios, such as segmenting portfolios or determining customized treatment paths. They could assess multiple factors like credit history and contact preferences. However, this form of agentic AI brings a number of potential compliance risks due to its advanced capabilities and is slower than reactive agents in general. 

Why they work:

• Facilitate strategic, personalized offers
• Support long-term engagement strategies
• Provide explainability in decision-making (important for compliance)

Potential weaknesses:

• Slower than reactive agents
• Require high-quality data and clear business rules to avoid compliance issues
• May need frequent recalibration to avoid outdated decisions

Best used for:

• Mid-stage collections and recoveries
• Strategic offer generation
• Prioritizing accounts based on ability and willingness to pay

3. Hybrid agents

Hybrid agents consist of a combination of reactive and deliberative elements, balancing quick responses with thoughtful planning. They can be highly effective in end-to-end collections software. For example, a hybrid agent might instantly react to a missed payment (reactive) whilst also adjusting the customer’s treatment path based on risk assessment and engagement history (deliberative).

Why they work:

• Best of both worlds: speed and strategy
• Support high-performance collections across the full customer journey
• Align well with AI-powered segmentation and automation engines

Best used for:

• Personalized collections journeys
• Combining compliance and customer care
• Adapting to pre-delinquency signals without sacrificing control

4. Collaborative agents

Collaborative agents work with other agents or humans to achieve shared goals. They often operate in multi-agent systems where coordination is key. These forms can be valuable in collector-assist tools. A potential example could be helping human reps in contact centers make better decisions or respond faster. They can also be used in workforce optimization to balance workloads or assign cases.

Why they work:

• Boost collection team productivity
• Enhance customer engagement through smarter human-AI interaction
• Improve consistency across large operations

Potential weaknesses:

• Complexity of coordination
• Must ensure data sharing and hand-offs are seamless
• Not ideal as stand-alone agents for customer interaction

Best used for:

• Contact center support
• Workflow orchestration
• Escalation management

5. Adaptive (learning) agents

These agents learn from their environment and improve their performance over time using techniques like reinforcement learning or neural networks. Potentially powerful for predicting delinquencies, optimizing strategies, and identifying at-risk accounts before default. However, they carry significant risk if not implemented with the proper guard rails. 

Why they’re risky:

• Compliance and explainability: Using sensitive customer data can be a compliance risk
• Regulatory concerns: Decisions must be explainable, auditable, and fair
• Data governance: Continuous learning demands rigorous data oversight

Best used for (with caution):

• Back-end analytics
• Portfolio trend analysis
• Forecasting delinquency curves rather than direct customer interaction

Choosing the right agent means choosing the right solution

In collections, every customer is different—and so is every account. That's why it's so important to choose the right AI model for every situation.

C&R Software's Agentic Framework makes it possible to create, test, and deploy AI agents within existing infrastructure. String together pre-built agents and Debt Manager functions to create custom AI agents for any use case. Leveraging Amazon Bedrock, the solution supports multi-model, multimodal AI agents that integrate ML, LLM, and Generative AI across the C&R tech stack.

Build trust, recover more, and stay compliant by letting AI work for you on your terms. Contact us at inquiries@crsoftware.com to find out more.