How to Get Started with Microsoft Discovery: A Step-by-Step Guide to Agentic R&D

Introduction

Microsoft Discovery is an enterprise-grade platform that harnesses the power of agentic AI to transform research and development (R&D) workflows. By deploying autonomous agent teams guided by human expertise, the platform enables researchers and engineers to reason over vast knowledge bases, generate hypotheses, test them at scale, and iterate rapidly. This guide walks you through the process of setting up and using Microsoft Discovery to accelerate your R&D initiatives, from initial access to running your first agentic loop.

What You Need

• An active Microsoft Azure subscription (or access to an Azure tenant)
• Administrator or contributor permissions to provision resources
• Basic familiarity with cloud concepts and R&D data management
• Organizational knowledge assets (structured data, documents, research papers) to feed into the platform
• Python or REST API knowledge (optional, for advanced customization)
• Microsoft Discovery preview access – apply through the official request process outlined below

Step 1: Request and Activate Microsoft Discovery Preview Access

1. Visit the Microsoft Discovery product page on the Azure portal or the Microsoft AI website.
2. Complete the preview request form with your organizational details, intended use cases, and estimated data volume.
3. Wait for approval – Microsoft reviews applications and grants access typically within a few business days.
4. Once approved, you will receive activation instructions via email. Follow the link to enable Microsoft Discovery in your Azure subscription.
5. Assign roles to team members: researchers, engineers, and data scientists each may need different permission levels (e.g., reader, contributor, administrator).

Tip: Ensure your Azure region supports the service – check the documentation for regional availability.

Step 2: Set Up Your Data Sources and Knowledge Base

1. Connect your organization’s datasets – use Azure Blob Storage, Azure Data Lake, or SQL databases to store structured and unstructured R&D data.
2. Index public-domain knowledge – ingest relevant scientific literature, patents, and technical reports into a searchable vector database.
3. Configure data connectors within the Discovery interface to link these sources. The platform supports pre-built connectors for common formats (PDF, CSV, JSON, etc.).
4. Organize by domain – create separate knowledge graphs for materials science, drug discovery, engineering simulations, or other focus areas.

Why this matters: Agentic AI relies on high-quality, up-to-date information to generate meaningful hypotheses and accurate simulations.

Step 3: Define Your Agent Team and Goals

1. Identify the R&D problem – e.g., “Find a sustainable polymer with tensile strength > 50 MPa and cost below $2/kg.”
2. Configure agent roles – assign specialized agents:
   • Hypothesis Generator – reasons over knowledge to propose candidates.
   • Validator – tests candidates against known data or simulation.
   • Analyzer – interprets results and suggests refinements.
   • Orchestrator – manages the loop and human handoff points.
3. Set constraints and success criteria (cost, performance, yield, compliance).
4. Define iteration limits – how many cycles before human review is mandatory.

Note: You can start with a simple two-agent design and expand as needed. The platform supports multi-agent orchestration out of the box.

Step 4: Run the Agentic Loop

1. Launch the agentic loop from the Microsoft Discovery dashboard. The orchestrator begins by feeding the goal to the Hypothesis Generator.
2. Monitor progress in real time – each agent logs its reasoning steps, actions, and intermediate results.
3. Allow agents to iterate:
   • Generate new hypotheses based on initial searches.
   • Test using simulations or historical data (if available).
   • Analyze outcomes and feed back into the next cycle.
4. Pause for human input when agents encounter uncertainties or reach predefined checkpoints.

Best practice: Start with a small number of candidates (e.g., 10-20) to validate the loop’s behavior before scaling up.

Step 5: Analyze Results and Iterate

1. Review the final report generated by the Analyzer agent. It includes:
   • Top-ranked candidates with supporting evidence.
   • Trade-off analysis (cost vs. performance, yield vs. compliance).
   • Confidence scores and alternative suggestions.
2. Export data for further validation – run physical experiments or high-fidelity simulations on promising candidates.
3. Refine the problem statement based on new insights. For example, adjust property targets or include additional constraints.
4. Re-run the loop with updated parameters to narrow down the solution space.

Tip: Use the platform’s visualization tools to explore the agentic decision tree and identify where human guidance had the most impact.

Step 6: Scale and Integrate into Your R&D Pipeline

1. Move from preview to production – once your proof-of-concept succeeds, work with Microsoft to migrate to a fully licensed tier.
2. Integrate with existing tools (e.g., laboratory information management systems, CAD software, or scientific computing environments) via APIs.
3. Train your team on interpreting agent outputs and providing effective feedback.
4. Establish governance – define when agents can make autonomous decisions and when human approval is required for critical milestones.

Outcome: Your organization now operates an agentic R&D center that continuously learns from past cycles, accelerating discovery and reducing manual iteration.

Tips for Success

• Start small – pick a well-defined, medium-complexity problem to build confidence before tackling grand challenges.
• Curate your knowledge base – garbage in, garbage out. Clean, relevant data dramatically improves agent reasoning.
• Collaborate across disciplines – involve domain experts (chemists, biologists, engineers) in configuring agent roles and validating hypotheses.
• Monitor costs – agentic loops can consume significant compute resources. Set spending limits and review usage regularly.
• Document failures – even unsuccessful runs provide valuable data to retrain agents and refine reasoning paths.
• Stay updated – Microsoft regularly releases new agent capabilities and connector improvements. Join the preview community for early access.

By following these steps, you can harness Microsoft Discovery to transform your R&D operations, enabling your team to pursue bolder ideas with faster, data-driven iterations. The future of scientific and engineering discovery is agentic – start building your first loop today.