Introduction
OpenAI’s GPT-5.5 is now generally available in Microsoft Foundry, bringing frontier-level intelligence to enterprise teams building production-ready AI agents. This guide walks you through the essential steps to set up, configure, and optimize GPT-5.5 within Foundry for high-stakes workflows. Whether you’re automating coding tasks, conducting deep research, or managing complex multi-step processes, you’ll learn how to leverage GPT-5.5’s improved reasoning, agentic execution, and token efficiency in a secure, governable platform.
What You Need
- Azure subscription with access to Microsoft Foundry (formerly Azure AI Studio).
- Permissions to create and manage AI resources in your Azure tenant.
- Basic familiarity with Azure Portal, Python SDK, or REST API calls.
- Use case definition – identify the agentic workflow (e.g., code generation, document analysis, research synthesis).
- Data sources (if needed) – documents, codebases, or databases that GPT-5.5 will access.
- Governance policies – security, compliance, and content filtering rules prepared.
Step-by-Step Guide
Step 1: Set Up Your Microsoft Foundry Environment
Start by provisioning a new project in Microsoft Foundry via the Azure Portal. Navigate to the Foundry hub, create a project with a unique name, and assign the necessary subscription, resource group, and region (e.g., East US or West Europe). Enable Azure AI services and link to your Azure OpenAI resource. This foundational step ensures you have the right compute and network configuration for GPT-5.5.
Step 2: Access and Deploy GPT-5.5
In your Foundry project, go to the Model Catalog and search for “GPT-5.5”. Select the version you need – standard or Pro. Click Deploy and choose a deployment name. Configure capacity (e.g., 10K TPM) based on expected traffic. For production use, enable content filtering and data loss prevention (DLP). Once deployed, note the endpoint URL and API key. This endpoint is the gateway for all agent interactions.
Step 3: Build Your Agent with the Foundry SDK
Use the Foundry Agent SDK (Python recommended) to create a custom agent that wraps GPT-5.5. Initialize the client with your endpoint and key. Define the agent’s instructions, tools, and memory. For example, an agent for coding tasks should include a code interpreter tool and a file system tool. Set the reasoning depth parameter to “high” for complex workflows. Integrate Microsoft 365 Graph if the agent needs access to documents or spreadsheets.
from foundry import FoundryClient, Agent
client = FoundryClient(endpoint="<your-endpoint>", credential="<your-key>")
agent = Agent(client, model="gpt-5.5-pro", reasoning_depth="high", tools=["code_interpreter", "file_search"])Step 4: Implement Long-Context Reasoning
GPT-5.5 excels at processing large codebases, lengthy documents, or multi-session histories. To harness this, structure your prompts with clear context windows. For example, when analyzing a 5000-line codebase, chunk the code into sections with summaries, then ask the model to reason across them. Use the context persistence feature in Foundry to maintain state across agent turns. Test with a sample that requires linking information from earlier steps – GPT-5.5 reliably keeps the thread.
Step 5: Optimize Token Efficiency
To reduce costs and latency, leverage GPT-5.5’s improved token efficiency. Set the max_tokens parameter to a realistic ceiling (e.g., 4096 for most tasks). Use structured output (JSON mode) to avoid verbose responses. Enable token caching in Foundry for repeated queries. Monitor token usage through Azure Cost Management and adjust the model variant (standard vs. Pro) based on your quality-speed trade-off. A good rule: use standard for high-volume, low-complexity tasks; Pro for critical analysis.
Step 6: Test, Monitor, and Iterate
Before going live, run a series of test scenarios that mirror your production workload. Use Foundry’s evaluation suite to measure accuracy, response time, and error rates. Set up Azure Monitor alerts for anomaly detection (e.g., sudden spikes in latency or token consumption). Collect user feedback and fine-tune your agent’s instructions, tool configuration, or context management. GPT-5.5 supports few-shot examples – include 3-5 high-quality examples in the system message to improve consistency.
Tips for Success
- Start with a well-defined, narrow pilot – e.g., automated code review for one repository – before scaling to multi-agent workflows.
- Always enable content filtering to comply with your organization’s security policies. Foundry provides built-in guardrails.
- Combine GPT-5.5 with other Foundry models for multi-model agents: use a lightweight model for classification and GPT-5.5 for reasoning.
- Version your agent configurations in a Git repository to track changes and roll back if needed.
- Monitor token cost per transaction and set budget alerts to avoid surprises. GPT-5.5’s efficiency reduces retries, but Pro can be expensive if overused.
- Leverage the Foundry community – sample code and best practices are available in the Microsoft documentation and GitHub.
- Test for edge cases like very long inputs, ambiguous instructions, or partial failures. GPT-5.5 recovers well, but your agent logic should handle retries gracefully.
By following these steps, you can transform GPT-5.5’s frontier intelligence into a reliable, cost-effective agent for your most demanding enterprise tasks. Let the platform handle the heavy lifting – you focus on the business value.