Custom AI Agent Development Services

Custom AI agent development services are needed to build and integrate autonomous AI agents that can reason, make decisions, and execute multi-step business tasks across your systems and data while complying with your business rules and security policies.

Why Build AI Agents With ScienceSoft

• 37 years in AI, software engineering, and business process automation.
• 21+ years of experience and hundreds of software projects in healthcare and insurance.
• Expertise in investments, lending, payments, finance, manufacturing, retail, telecoms, and 20 other industries.
• 750+ specialists, including senior AI architects, data scientists, machine learning engineers, software developers, security experts, and DevSecOps engineers with 9–20 years of experience.
• An Architecture and Solutions CoE that continuously extracts and enforces best practices for designing AI systems that fully justify investments and remain resilient over time.
• A long-established PMO that applies lessons learned from thousands of our enterprise IT projects to control scope, priorities, quality, and budget in complex, fast-moving AI initiatives.
• In-house compliance consultants that help align AI solutions with global standards (e.g., SOC 2, PCI DSS/SSF, NIST), regional privacy rules (e.g., GDPR, Saudi PDPL), and sectoral regulations (e.g., HIPAA, FDA QMSR, SOX, GLBA, NYDFS).
• In-house compliance consultants to align AI solutions with global standards (e.g., SOC 2, PCI DSS/SSF, NIST), regional privacy rules (e.g., GDPR, Saudi PDPL), and sectoral regulations (e.g., HIPAA, FDA QMSR, SOX, GLBA, NYDFS).

AI Agent vs. AI Chatbot vs. AI Workflow

Sample Architecture for a Customer Support Agent

Below, ScienceSoft’s solution architects describe a real-world architecture for a customer support AI agent designed to automate customer service operations across digital channels. The agent can handle a wide range of post-purchase interactions, including order tracking, returns and refunds, billing inquiries, product troubleshooting, account updates, and proactive customer notifications.

The same architectural principles apply to other customer-facing AI agents, such as insurance claims agents, banking service agents, or healthcare patient support agents. The main differences are the business systems involved, authentication requirements, regulatory controls, and permitted actions. The core design remains consistent: separating agent responsibilities, securely identifying users, controlling access to customer data, enforcing business rules before executing actions, and connecting the agent to trusted enterprise systems through controlled interfaces.

The AI agent is embedded into customer communication channels, including web chat, mobile applications, messaging platforms, and voice interfaces. Each interaction begins with customer identification and context resolution through authentication mechanisms such as OAuth2, JWT-based authentication, customer account lookup, or session-based verification. This allows the agent to determine the customer's account context, available services, previous interactions, and permitted actions before processing a request.

At the center of the architecture is an AI agent orchestrator that coordinates all customer interactions and backend operations. It interprets customer intent, maintains interaction context, selects the appropriate tools, applies business rules, and manages task execution from request to completion. Rather than allowing the LLM to directly access business systems, the orchestrator controls which capabilities are available to the agent, which actions require additional verification, and when human involvement is required. It determines whether a request should be answered from the knowledge base, resolved through a business transaction, or escalated to a support specialist. For example, a request such as “Where is my order?” may trigger a read-only lookup in the order management system, while “I want a refund” may initiate a structured refund workflow with eligibility checks, approval rules, and fraud validation.

Task-specific agents execute narrowly defined customer service operations within controlled boundaries. Each agent is connected only to the systems and tools required for its responsibility and operates with the minimum permissions necessary. For transactional requests, task-specific agents connect to enterprise systems such as:

• Order management systems (Shopify, SAP Commerce, Adobe Commerce) for order tracking, cancellations, shipment status, and fulfillment updates.
• CRM and customer service platforms (Salesforce Service Cloud, Zendesk, Dynamics 365 Customer Service) for customer profiles, cases, interaction history, and escalation management.
• Payment and billing systems (Stripe, Adyen, ERP billing modules) for invoice inquiries, payment status checks, and refund processing.
• Returns management systems for return eligibility checks, RMA creation, and return status tracking.

For information-based requests, the orchestrator routes queries to approved knowledge sources, including product documentation, FAQs, troubleshooting guides, warranty policies, shipping rules, and service procedures. Retrieval combines semantic search, keyword search, metadata filtering, document ranking, and, where required, live system queries. Access controls ensure that the agent retrieves only information appropriate for the customer, region, product type, account status, or support context. For example, the agent may use product documentation to explain troubleshooting steps while simultaneously retrieving account-specific warranty information from CRM or order systems.

A proactive communication module allows the AI agent to initiate customer interactions based on predefined business events rather than waiting for customer requests. For example, the AI agent can notify customer about shipment delays, remind them about incomplete return requests, or inform them about service appointment changes. Events from order management, CRM, logistics, or monitoring systems trigger predefined agent workflows, while business rules determine when and how customers should be contacted.

Certain customer actions require human review based on business rules, financial thresholds, risk indicators, or regulatory requirements. An approval and escalation engine manages these scenarios by routing requests to appropriate support teams and providing the necessary context (customer information, conversation history, requested action, retrieved records, validation results, etc.). Examples include refunds above a configured threshold, account changes with security implications, suspected fraud cases, and unresolved issues after repeated attempts. The AI agent continues to manage the interaction context but does not complete restricted actions until required approval is received.

A guardrails layer protects customer data and controls agent behavior across the entire interaction lifecycle. Before processing requests or executing actions, the guardrails layer verifies:

• Customer identity and authentication status.
• Available permissions.
• Allowed operations for the current context.
• Applicable business policies.

It also enforces technical and data protection controls, including:

• PII detection and masking before information is sent to AI models or stored in logs.
• Removal of payment card details and other sensitive identifiers from prompts and observability data.
• Prevention of unauthorized account changes.
• Rate limiting and abuse prevention.

After execution, the same layer can inspect outputs, verify that responses do not expose restricted information, and attach audit metadata.

An observability layer captures the complete execution trail of customer interactions, including customer requests, detected intent, retrieved information sources, tool calls, business actions performed, approvals and escalations, and final responses. This data is stored in secure audit repositories and used for troubleshooting, compliance reporting, performance analysis, and agent optimization. Operational metrics such as resolution rate, escalation frequency, failed tool calls, response accuracy, and customer feedback help identify where the agent requires improvement and support continuous refinement of prompts, workflows, knowledge sources, and integrations.

AI Agent Development Process