Build 2026: あらゆるフレームワークのAIエージェントで可観測性からROIまでBuild 2026: From observability to ROI for AI agents on any framework

Microsoft used its Build 2026 developer conference to push a message that has become central to enterprise AI: deploying an agent is comparatively simple, but operating one reliably in production is where most teams struggle. In a Microsoft Foundry blog post, Sebastian Kohlmeier frames the problem around a single uncomfortable fact about modern agents. They are non-deterministic, meaning the same input can produce different outputs across runs, which complicates the accuracy, safety, and accountability that businesses expect from software they depend on.

That non-determinism is the crux of the observability challenge. Traditional application monitoring assumes predictable execution paths, fixed inputs and outputs, and error states that can be reproduced. Agents built on large language models behave differently. They reason over multiple steps, call external tools, retrieve documents, and sometimes loop or branch in ways that are difficult to anticipate. When something goes wrong, engineers need to see not just that a failure occurred but why the agent made a particular decision, which tool it invoked, what context it retrieved, and how its reasoning unfolded. Without that visibility, debugging becomes guesswork and governance becomes nearly impossible.

The Foundry approach, as described, treats observability as the foundation rather than an afterthought. The emphasis on supporting agents on any framework is notable. Many teams have already built on open-source orchestration libraries such as LangChain, LlamaIndex, Semantic Kernel, or AutoGen, and they are reluctant to rewrite working systems to fit a single vendor's runtime. By positioning its tooling as framework-agnostic, Microsoft appears to be acknowledging that enterprises run heterogeneous stacks and wants Foundry to act as a monitoring and evaluation layer above whatever orchestration code already exists. This is consistent with the broader industry shift toward standardized telemetry, where OpenTelemetry conventions for generative AI and agent traces are increasingly used to capture spans for model calls, tool usage, and token consumption in a portable way.

Beyond raw tracing, the article points toward a pipeline that runs from observability through evaluation to return on investment. Tracing answers what happened. Evaluation asks whether the outcome was good, using techniques such as automated scoring, LLM-as-a-judge methods, and human review against defined criteria like factual accuracy, relevance, safety, and adherence to policy. These evaluations can be run continuously in production or as part of pre-deployment testing, allowing teams to catch regressions when a model version changes or a prompt is updated. Safety and accountability fit into this layer as well, since logging decisions and grounding data creates an audit trail that compliance and risk teams can inspect.

The final and arguably hardest step is connecting all of this to ROI. Measuring the business value of an agent is more nuanced than tracking uptime or latency. Organizations want to know whether an agent resolves customer issues, reduces manual workload, or shortens process times, and whether it does so at an acceptable cost given token pricing and infrastructure. Linking operational telemetry to outcome metrics is where many deployments stall, because the data lives in separate systems. Foundry's pitch appears to be that a unified view from trace to outcome makes that link easier to establish, though the practical value will depend heavily on how teams instrument their own success metrics.

This positioning sits within a competitive landscape. Specialized observability vendors such as LangSmith, Arize, and Langfuse have built businesses around tracing and evaluating LLM applications, and cloud rivals including Amazon and Google offer their own agent tooling. Microsoft's advantage is likely its integration with Azure, its enterprise customer base, and the broader Foundry portfolio that spans model access, agent building, and deployment. The strategic logic is that customers who already evaluate and monitor inside Foundry are more likely to keep their production workloads there.

For practitioners, the underlying lesson is durable regardless of which vendor they choose. Production AI agents demand the same operational discipline as any critical system, plus additional layers for non-deterministic behavior. Establishing tracing, defining evaluation criteria early, and tying performance back to business outcomes are becoming prerequisites for moving agents beyond pilots. Whether Foundry delivers on the full observability-to-ROI promise will become clearer as teams report real deployment results in the months ahead.