Microsoft Discovery is transforming research and development by enabling teams to harness the power of agentic AI. This platform allows autonomous agent teams, guided by human expertise, to reason over vast knowledge bases, generate hypotheses, test them, analyze results, and iterate—all at scale. For scientists and engineers, this means accelerating the journey from concept to outcome, whether developing sustainable materials, clean energy sources, or effective treatments. This step-by-step guide will show you how to set up and run agentic R&D workflows using Microsoft Discovery, turning ambition into tangible results.
What You Need
- An active Microsoft Azure subscription with permissions to create resources.
- Access to Microsoft Discovery (preview); contact Microsoft for onboarding.
- A team of domain experts (e.g., chemists, engineers, data scientists) to guide the agents.
- Organizational knowledge bases or public-domain datasets relevant to your R&D field.
- Basic familiarity with cloud AI services and agentic architectures.
Step-by-Step Instructions
Step 1: Define Your R&D Challenge and Scope
Begin by clearly articulating the scientific or engineering problem you want to solve. Agentic AI works best when goals are specific, measurable, and aligned with organizational priorities. For example: “Identify a new catalyst that reduces production cost by 20% while maintaining yield.” Document the constraints (cost, performance, regulatory) and success criteria. This will guide your agent teams throughout the discovery loop.
Step 2: Assemble Your Agent Team
Within Microsoft Discovery, create specialized agents that reflect different R&D roles. Typical agents include:
- Knowledge Agent: Retrieves and synthesizes information from internal databases and public literature.
- Hypothesis Agent: Generates novel hypotheses by reasoning across domains and constraints.
- Experiment Agent: Designs and simulates tests (virtual or physical) to validate hypotheses.
- Analysis Agent: Interprets test results and feeds conclusions back into the loop.
Configure each agent with appropriate tools and data sources. Assign human supervisors to monitor and adjust agent behavior as needed.
Step 3: Ingest and Structure Knowledge
Connect Microsoft Discovery to your organization’s knowledge repositories—research papers, patents, experimental data, lab notebooks, and public-domain datasets. Use the platform’s data connectors to index and structure this information. Ensure the knowledge base is curated and up-to-date; agent reasoning quality depends on the data it can access. For example, upload historical synthesis procedures or material property databases.
Step 4: Define the Agentic Loop Workflow
Set up the iterative process that agents will follow. Using the Discovery API or GUI, define a loop:
- Reason and Retrieve: The Knowledge Agent searches for relevant prior work and constraints.
- Hypothesize: The Hypothesis Agent proposes candidates (e.g., new molecular structures, alloy compositions).
- Test: The Experiment Agent runs simulations, computational models, or triggers real-lab workflows.
- Analyze: The Analysis Agent evaluates outcomes against success criteria and identifies promising directions.
- Iterate: The loop repeats, with each cycle refining the search space based on new data.
Configure the loop termination condition (e.g., after 100 iterations or when a candidate meets all constraints).
Step 5: Run Initial Simulations and Validate
Launch a pilot run with a small set of agents and a limited search space. Monitor the process in real time using the Microsoft Discovery dashboard. Check that agents are retrieving correct information and that reasoning steps make sense. Validate initial outputs with domain experts—this ensures the loop is on track before scaling up. For instance, review the first 10 hypotheses and their initial test results.
Step 6: Integrate Human Feedback
Agentic AI is most powerful when humans remain in the loop. Use the platform’s annotation and feedback tools to guide agents toward fruitful directions. For example, if an agent suggests a material that is toxic or unstable, mark it as invalid and add a note explaining why. Over time, agents learn from these corrections, reducing false leads. Schedule periodic reviews where the R&D team discusses top-ranked candidates and adjusts parameters.
Step 7: Scale the Agentic Loop
Once the pilot is validated, expand the effort. Increase the number of agents, widen the search space, and add more data sources. Microsoft Discovery runs on Azure’s high-performance infrastructure, so you can scale compute resources on demand. Consider parallelizing multiple loops for different sub-problems—for example, one loop for materials synthesis and another for performance testing. This accelerates time to insight.
Step 8: Analyze Results and Refine
After the loop completes (or reaches a milestone), analyze the final candidates. Use the Analysis Agent’s reports combined with human expertise to select the most promising outcomes. Document the reasoning path for each candidate—this creates a valuable knowledge asset for future projects. Microsoft Discovery provides audit trails, so you can trace how each hypothesis emerged and was tested. Refine your approach (e.g., adjust reward functions or add new constraints) and restart the loop if needed.
Step 9: Operationalize and Share Insights
When a validated candidate emerges (e.g., a new reaction pathway or material formulation), work with your engineering team to bring it into production. Microsoft Discovery integrates with Azure DevOps and other CI/CD tools to facilitate handoff. Share the agentic R&D process and findings across the organization to foster a culture of AI-augmented discovery. Use the platform to generate reports and visualizations for stakeholders.
Tips for Success
- Start with a well-defined problem. Avoid overly broad goals; focus on a specific R&D bottleneck (e.g., screening 10,000 potential catalysts) where agentic AI can deliver quick wins.
- Invest in knowledge curation. The quality of agent reasoning depends on clean, structured, and comprehensive data. Dedicate time to cleansing and tagging your datasets before starting the loop.
- Keep humans engaged. Domain expert oversight prevents agents from going down unrealistic paths. Schedule regular checkpoints to review agent outputs and provide feedback.
- Monitor cost and performance. Agentic loops can consume significant compute. Set budget alerts and optimize agent tasks (e.g., use simpler models for initial screening, then higher-fidelity simulations for top candidates).
- Iterate on the loop design. The first workflow you design may need adjustments. Experiment with different agent compositions, reward functions, and termination criteria to improve efficiency.
- Collaborate with Microsoft support. Since Microsoft Discovery is in preview, leverage Microsoft’s documentation and support channels to troubleshoot and learn best practices from early adopters.
- Plan for organizational change. Introduce your team to the concept of agentic R&D. Address concerns about job roles by emphasizing that agents augment, not replace, human creativity and expertise.