AI Agent Development Services

A proof of concept tests technical feasibility. A pilot tests one defined workflow with representative data, limited permissions, and agreed evaluation criteria. We use the appropriate format based on what the business needs to confirm.

The work identifies where the agent completes tasks reliably, where it needs more context, and which actions should remain under human review before a wider deployment.

Includes:

• PoC or pilot scope
• Representative data and test cases
• Working agent workflow
• Human approval and escalation rules
• Quality and performance evaluation
• Production-readiness recommendations

Our custom AI agent development services cover the architecture, workflow logic, instructions, retrieval, tool access, and interfaces required for a defined business process.

The implementation defines what the agent can access, which actions it can take, how results are checked, and what happens when information is missing or uncertain.

Includes:

• Agent and workflow architecture
• Prompt and instruction design
• Retrieval and memory setup
• Tool and API configuration
• Permissions and review points
• Evaluation, QA, and documentation

We develop LLM agents that retrieve context, use approved tools, and complete multi-step tasks. Depending on the workflow, one agent may handle the full sequence or several specialized agents may coordinate extraction, validation, routing, and reporting.

For autonomous AI agents, we define the permitted action scope explicitly. High-impact, sensitive, or uncertain actions can require confirmation or escalation before execution.

Includes:

• LLM and model selection
• Single-agent and multi-agent design
• Tool-calling workflows
• Context and state management
• Routing and orchestration logic
• Action limits and fallback behavior

Our AI agent integration services connect agents to CRMs, ERPs, support platforms, knowledge bases, databases, email, document storage, and internal APIs. Read and write access is configured according to the workflow and user permissions.

AI agent deployment services cover environment configuration, release controls, logging, monitoring, and operational handover for the target infrastructure.

Includes:

• API, webhook, and data connections
• Authentication and permission setup
• Cross-system workflow implementation
• Deployment configuration
• Logging and monitoring setup
• Integration and operations documentation

We define how the agent is assessed and controlled before it handles production work. The setup covers access boundaries, approval rules, evaluation scenarios, fallback behavior, activity records, and ownership of exceptions.

Evaluation combines task-level quality checks with operational measures such as completion rate, processing time, escalation rate, latency, and cost where relevant.

Includes:

• Data and action boundaries
• Role-based access rules
• Human approval workflows
• Evaluation datasets and scenarios
• Failure and escalation handling
• Audit and review requirements

After deployment, we review production performance, user feedback, errors, latency, usage, and operating cost. Findings are used to adjust instructions, retrieval, routing, model selection, integrations, and review rules.

Managed support can also cover production monitoring, issue investigation, documentation, workflow changes, and expansion into additional tasks or teams.

Includes:

• Production monitoring and issue review
• Quality and task-completion tracking
• Prompt, retrieval, and routing updates
• Latency and cost optimization
• Integration maintenance
• Improvement and expansion planning