Cloud AI agents and intelligent automation
Connect cloud-based AI agents to the systems, robots, APIs, documents, queues, and people that run your business. BotDev designs governed agentic workflows where AI interprets and coordinates while RPA performs controlled, repeatable execution.
BotDev - cloud AI and RPA specialists
Cloud AI agents become useful when they can complete work, not just generate text. We connect agent reasoning to approved tools, RPA robots, APIs, queues, document workflows, and human approvals.
Talk to Cloud AI Experts ->Cloud agent foundation
Start with a bounded business responsibility instead of an open-ended chatbot. Define the inputs, context, tools, allowed actions, decision points, output, and success measures.
RPA integration
An agent can understand a request, select an approved action, and send structured work to a robot or queue. The RPA workflow handles the exact screen, file, or system transaction and returns evidence of what happened.
Knowledge and documents
Connect the agent to approved documents, policies, knowledge bases, and structured records so it can retrieve relevant context before making a recommendation or routing work.
Human-centered operations
Cloud AI agents should not silently take actions outside their intended scope. BotDev helps teams introduce approval gates, review queues, exception paths, and clear ownership so people can supervise the workflow where judgment, policy, or customer impact requires it.
From cloud agent to production workflow
The architecture should separate interpretation from execution. This makes the system easier to test, secure, monitor, and improve as the use case grows.
Agent reads the request and retrieves approved context.
Agent chooses an allowed tool or asks for clarification.
RPA, API, or workflow service performs the transaction.
System returns evidence, status, and next-step routing.
Choose the cloud services, model endpoints, data boundaries, environments, and retention rules that fit the workflow. Keep the design portable enough to change models or providers without rebuilding every downstream process.
Expose only the actions an agent needs through a controlled tool layer. A tool may query a system, create a queue item, request a robot run, retrieve a document, or start an approval process.
Coordinate long-running work across agents, bots, queues, systems, and people. Monitor the entire business outcome rather than only the model response.
Test real examples, edge cases, tool selection, security boundaries, and business outcomes before expanding the workflow. Capture corrections and exceptions as feedback for better rules, prompts, retrieval, or process design.
Yes. The agent can submit structured work to an approved queue, workflow, or API that starts the RPA action. The robot should return a status and evidence rather than leaving the agent to guess whether the transaction completed.
Usually, deterministic RPA or APIs are easier to secure and test for system transactions. An agent can decide when to use an approved action while the RPA layer handles the exact interaction.
Common patterns include service-request triage, document intake, invoice and claims preparation, employee onboarding, exception classification, knowledge-assisted operations, and workflow routing.
Yes. Our certified engineers can support architecture, integration, testing, orchestration, monitoring, incident response, documentation, and continuous improvement in US-aligned time zones.
Move from AI experiment to business workflow
Bring us a process that needs better interpretation and reliable execution. BotDev can help define the agent, tools, RPA integration, controls, and delivery team at a fraction of the cost of building every capability locally.
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