Build production-grade agent workflows using OpenAI’s Agents platform - code-first implementations with the Agents SDK, tooling integration, tracing, and quality evaluation.
OpenAI describes agents as systems that accomplish tasks by combining a model with tools, additional context, and workflow logic. OpenAI’s Agents SDK is a code-first library intended to make it straightforward to build agentic applications - including tool use, handoffs between specialised agents, streaming, and full tracing of what happened - while OpenAI’s AgentKit provides a set of tools to build, deploy, and optimise agent workflows.
LW IT Solutions builds agent workflows that are safe, observable, and aligned to real business processes. We design the agent’s objectives and operating boundaries, integrate approved tools (including MCP where appropriate), implement evaluation and regression testing patterns, and establish an operational model for change and monitoring. The result is an agent solution that can be adopted confidently - avoiding unmanaged tool access, unclear ownership, and ‘black box’ behaviour that creates risk.
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Service Overview
Highlights
- Code-first agent builds using OpenAI Agents SDK
- Tool calling with controlled permissions and auditability
- Support for multi-agent workflows and handoffs
- Built-in tracing for visibility into agent decisions and actions
- Evaluation and regression testing to manage change safely
Business Benefits
- Deliver agent workflows that map to real business processes with clear boundaries and ownership
- Reduce risk through controlled tool access, approvals, and full execution tracing
- Improve reliability and confidence with evaluation criteria and regression testing
- Speed up delivery using code-first patterns that fit existing engineering practices
- Maintain visibility and supportability through monitoring, runbooks, and change governance
Typical use cases
- Internal assistants that query systems and take controlled actions
- Case handling agents for support, operations, or security triage
- Document or data processing agents with tool-based validation steps
- Developer productivity agents integrated with repositories and CI systems
- AI workflows requiring traceability for audit or assurance
Objectives & deliverables
What Success Looks Like
- Implement agent workflows that perform defined tasks with clear limits
- Ensure tool access is governed, observable, and auditable
- Establish quality evaluation to detect regressions and drift
- Provide an operating model for support, change, and improvement
- Create a repeatable pattern for adding new agents and tools
What You Get
- Agent design pack: use case scope, boundaries, tools, and governance model
- Implemented agent workflow (Agents SDK codebase) with documented tool integrations
- Operational readiness pack: tracing approach, monitoring/alerting, and runbooks
- Evaluation pack: acceptance criteria, regression tests, and improvement backlog
- Handover and enablement session for owners and support teams
How It Works
- Discovery - confirm use case, stakeholders, constraints, and success measures.
- Design - define agent boundaries, tools, workflow logic, and approval gates.
- Build - implement the agent using the Agents SDK and integrate required tools (including MCP where appropriate).
- Evaluate - validate quality against acceptance criteria; establish regression tests and monitoring metrics.
- Operationalise - implement tracing, runbooks, and ownership model; confirm change governance.
- Handover - enable the customer team and agree next-step enhancement roadmap.
Engagement Options
- Agent Proof of Value - single agent workflow with limited tools and evaluation
- Production Build - full agent implementation with tracing, tests, and runbooks
- Multi-Agent Orchestration - coordinated specialist agents with handoffs and controls
- Operate - ongoing support, tuning, and tool onboarding under governance
Common Bundles
Customers who use this service often bundle with these services
AI Safety, Governance & Risk
Implement practical AI safety and governance with policies, approvals, logging, data boundaries, and controls that reduce operational and compliance risk.
Prompt Evaluation & Testing
Prompt evaluation and testing service defining acceptance criteria, golden datasets, regression checks and quality metrics to control AI outputs.
MCP Server Builds & Tool Integrations
Build secure MCP servers and tool integrations that expose data and actions to AI agents with governed access and deployment.
RAG / Chat with Your Data
Build governed RAG chat with your data solutions using secure retrieval, permissions-aware context, and measurable answer quality controls.
API & System Integrations
Design and implement API integrations connecting business systems with secure authentication, retries, logging, and supportable middleware patterns operations.
Frequently Asked Questions
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