Deploy AI Agents That Do Real Work WithForward Deployed AI Agent Engineers
A chatbot can answer questions. An AI agent can also use approved tools to complete parts of a workflow. SDI’s forward deployed AI agent engineers work with your team to connect agents to business systems, define safe actions, and move from demonstrations to controlled production use.
Agent Deployment in Real Workflows
Why do AI agents impress in demosbut fail in daily operations?
A good demo rarely includes missing records, conflicting instructions, expired credentials, or a customer asking for an exception. SDI engineers design for those realities by mapping what the agent may do, the tools it needs, and when a person should take over.
Turn a promising agent concept into a dependable process your staff can supervise and improve.
SDI team augmentation starts at $3,999/month; ask about a forward deployed engineering engagement.
Tool Calling & Human Approval
What should an AI agent beallowed to do in your business?
An agent might prepare a CRM update, draft a support response, categorize an intake request, or gather information from approved systems. Engineers can implement tool calling, scoped API permissions, Model Context Protocol (MCP) connections where appropriate, and human approvals before consequential actions.
Keep permissions explicit, preserve audit trails, and give employees a clear way to review, correct, or stop an agent.
Agents Built for Operations
Which types of agents areready for a practical rollout?
Start with a narrow job, defined access, and a measurable standard for success.
Service Agents
RESOLVE FASTER
Retrieve approved knowledge, collect missing details, draft replies, and route support cases to the right person.
Operations Agents
MOVE WORK FORWARD
Prepare task updates, compare records, assemble reports, and suggest next steps across authorized systems.
Agent Guardrails
KEEP CONTROL
Add tool restrictions, evaluations, tracing, human approval, and escalation paths for uncertain or sensitive tasks.
Agent Orchestration & Reliability
How do you know an agent isworking correctly after launch?
SDI can test real scenarios, examine traces, measure tool-call success, and revise workflows as exceptions appear. Depending on the architecture, engineers may use OpenAI Agents SDK, LangGraph, MCP-enabled tools, or custom orchestration rather than forcing every use case into one framework.
The objective is not an autonomous agent at any cost; it is useful automation with clear oversight, predictable handoffs, and an accountable owner.