Overview of Building more than just an agent harness
In this Stack Overflow Podcast episode recorded at Microsoft Build, host Ryan Donovan speaks with Jay Parikh, VP of AI Core at Microsoft, about what it really takes to build, deploy, secure, and optimize AI agents at enterprise scale. The discussion goes beyond “agent harnesses” and focuses on the full platform picture: local development, enterprise data integration, observability, governance, model routing, evaluation, and the cultural shift required to adopt agentic software.
Key Themes and Takeaways
Agents need an end-to-end system, not just a model or harness
Jay’s main point is that enterprise agents are not successful as isolated components. To work in real business settings, they need:
- A development environment for building and testing locally
- Connection to enterprise data for grounding and correctness
- A runtime platform for deploying and operating agents
- Security, governance, and observability
- Human-in-the-loop workflows where needed
He frames Microsoft’s approach as a full continuum: build → deploy → run → improve.
Foundry is positioned as the enterprise agent platform
Microsoft Foundry is presented as the platform where agents move from prototype to production. It provides:
- Model choice
- Long-running agent support
- Observability and telemetry
- Guardrails and red-team testing
- Integration with enterprise security and workflow systems
The goal is to give organizations a controlled, scalable way to run large numbers of agents across business processes.
Building Agents: From Local Dev to Production
Local-first development is still important
Developers can start in familiar tools like:
- VS Code
- The GitHub app
- Python and open-source frameworks
- Their own custom harnesses or SDKs
Jay emphasizes that Microsoft is not forcing a single approach. Builders can bring their own tools, agent loops, evals, and frameworks.
Hybrid local + cloud development
A major theme is the hybrid workflow:
- Use local hardware and local models to prototype quickly
- Run lightweight or lower-cost tasks locally
- Send more demanding workloads to the cloud
- Deploy to Foundry once the agent is tested and ready
This is framed almost like a new CI/CD pipeline for agents.
Windows and local AI development
The episode highlights improved Windows developer experiences for AI work, including:
- Faster setup
- AI-powered CLI tooling
- Local model execution
- Easier iteration before pushing into enterprise deployment
Evaluations, Guardrails, and Reliability
Evals are central to agent quality
Jay repeatedly stresses that evals are one of the most important parts of the system. Good evals help answer:
- Is the agent doing the right thing?
- Is it staying within policy?
- Is it improving over time?
- Is it fit for the specific business use case?
He notes that starting with benchmarks is useful, but enterprise evals quickly become more specialized and must be tailored to each organization’s data, brand, workflows, and goals.
Red-team testing and continuous validation
Foundry includes ways to:
- Run red-team agents against your agent
- Test for failure modes and policy violations
- Identify weak spots in prompts, tool calls, context, or skills
- Continuously evaluate agents after deployment, not just at launch
This makes reliability an ongoing process rather than a one-time validation step.
Enterprise Security, Governance, and Scale
Agents must fit into existing security systems
A major enterprise requirement is governance. Jay says agents should integrate with the tools organizations already use, including:
- Entra for identity and registration
- Purview for data governance
- Defender and the Microsoft security stack
When an agent is deployed, it should behave like a managed enterprise identity, not an unmanaged script.
Scale means thousands of agents, not just a few
The conversation emphasizes that enterprises may eventually run tens of thousands of agents, which creates a need for:
- Versioning
- Tracking
- Access control
- Debugging
- Monitoring
- Centralized governance of tools and permissions
Jay compares this to a kind of service mesh or operating system for agents.
Models, Optimization, and Cost Control
New Microsoft models support different workloads
Jay mentions a set of newly announced models, including:
- A coding model
- A reasoning/thinking model
- Other multimodal models for transcription, voice, and imaging
These models are meant to plug into the same agent platform and improve over time through telemetry and enterprise feedback loops.
Cost management is built into the platform
The discussion also addresses the real cost of AI usage. Microsoft’s approach includes:
- Local model execution for cheaper or faster tasks
- Auto-routing to the best model based on intent and budget
- A model router in Foundry to optimize for cost, quality, and latency
- Visibility into token usage and model serving
Jay emphasizes that cost should be balanced against business value, and that enterprises need tools to make those tradeoffs deliberately.
Infrastructure-level optimization matters too
Beyond model selection, Microsoft is also applying low-level efficiency techniques such as:
- Prompt caching
- KV caching
- Decoding optimizations
- Hardware-aware inference tuning
The idea is to reduce runtime costs while preserving quality and performance.
Frontier Challenges and Industry Shifts
The hard problems are technical and cultural
Jay identifies two major frontier challenges:
-
Technical maturity
- Easier agent development
- Longer-running agents
- More complex tasks
- Better automation and optimization
-
Organizational change
- New ways of thinking about software development
- More experimentation
- Faster decision-making
- Less toil, but more iteration
- A compressed SDLC that changes how teams work
His point is that AI agents are not just a tooling shift — they require a mindset shift.
Resources and Where to Try It
What listeners can explore
Jay points listeners to:
- The GitHub app for trying the coding experience
- Microsoft’s Azure AI Foundry site for building agents and AI apps
- Foundry features for:
- Voice
- Content understanding
- Agent building
- Eval authoring
Contact
Jay says people can connect with him on LinkedIn.
Bottom Line
This episode argues that successful enterprise AI agents require more than a harness or a single model. They need a full platform that supports local development, secure deployment, continuous evaluation, cost optimization, and enterprise governance. Microsoft’s pitch is that Foundry and its surrounding tools provide that end-to-end system for the agentic era.