Shipping an AI agent is often just the beginning. The real challenge lies in keeping that agent accurate, safe, and accountable once it's in production. Unlike traditional software, AI agents exhibit non-deterministic behavior that shifts as underlying models update, tools evolve, and usage patterns change—usually silently after the initial demo phase.

Microsoft Foundry’s latest advancements, unveiled at Build 2026, focus on closing that gap with a comprehensive, end-to-end observability platform. This platform covers the full AI agent development lifecycle: from the earliest inference calls to delivering a CFO-ready ROI dashboard. Foundry now supports observability and evaluation capabilities for AI agents built on any framework and deployed anywhere, making it easier for developers to build trustworthy, continuously improving agents without switching ecosystems or facing siloed monitoring.

This post dives deep into the key capabilities introduced at Build 2026, including interoperability across popular agent frameworks, multi-turn and rubric-based evaluations, code-first observability integrated with developer tools, evidence-backed optimization loops, and business metrics tied directly to agent performance. If you’re building AI agents at scale or managing a portfolio of AI-powered workflows, understanding these innovations is critical to reliably shipping and improving your agents in production.

Architecture Overview

┌──────────────────────────────────────────────┐
│          Enterprise AI Workloads              │
├──────────────────────────────────────────────┤
│  • Diverse agent frameworks (LangChain,       │
│    Microsoft Agent Framework, OpenAI SDK)     │
│  • Multi-tool orchestration & integrations     │
└──────────────────────────────────────────────┘
                      ↓
┌──────────────────────────────────────────────┐
│           Microsoft Foundry Platform           │
├──────────────────────────────────────────────┤
│  • Unified tracing via OpenTelemetry           │
│  • Multi-turn, rubric-based evaluations        │
│  • Azure Developer CLI integration              │
│  • Intelligent trace sampling                   │
│  • Agent optimization & deployment loops       │
│  • ROI measurement & dashboards                 │
└──────────────────────────────────────────────┘
                      ↓
┌──────────────────────────────────────────────┐
│           Developer & Business Tools           │
├──────────────────────────────────────────────┤
│  • Azure Monitor alerts & dashboards           │
│  • CLI & VS Code debugging workflows            │
│  • ROI dashboards for stakeholders              │
│  • Incident trace replay & root cause analysis │
└──────────────────────────────────────────────┘

This architecture allows every agent step—from prompt crafting, language model calls, to tool invocations—to be traced and evaluated, regardless of the underlying framework or orchestration tool. The continuous feedback loop ensures developers can monitor, optimize, and prove the value of their AI agents seamlessly.

Image source: Microsoft Foundry Blog, Build 2026

Key Technical Observations

• Cross-Framework Interoperability Through OpenTelemetry
  By adopting OpenTelemetry as a common telemetry standard, Foundry enables unified tracing and evaluation across heterogeneous agent frameworks such as LangChain, Microsoft Agent Framework, and OpenAI SDK. This approach avoids vendor lock-ins and framework silos, providing a consistent observability plane without forcing developers to rewrite their agents.

• Multi-Turn and Context-Aware Evaluations
  Traditional single-turn evaluations fail to capture failure modes that emerge over a conversation, like tone drift or goal loss. Foundry's multi-turn evaluation capability scores entire conversations, measuring reasoning consistency and end-to-end task success, enabling a much richer assessment of agent quality and safety.

• Rubric Evaluator That Customizes Quality Criteria
  The rubric evaluator generates context-sensitive, weighted evaluation criteria tailored to an agent’s role—such as vendor history or compliance review agents. This makes quality scoring relevant and business-aligned, integrating safety, tone, cost, latency, and task completion into a holistic scorecard that runs continuously.

• Developer-Centric Observability Integration
  Observability insights—including traces, eval results, and failure diagnoses—are embedded directly into the Azure Developer CLI (azd) and Visual Studio Code experience. This “code-first” approach streamlines debugging, enabling developers to test, diagnose, and iterate without leaving their workflow.

• Continuous Optimization Backed by Evidence
  The Agent Optimizer replaces guesswork with a controlled, evidence-backed optimization loop. It analyzes current agent configurations, searches for improved variants, ranks candidates with detailed diffs and audit trails, and evaluates them against the rubric—all automating the improve-ship cycle with rollback options.

• Quantifiable ROI Integrated Into Agent Management
  Foundry uniquely links agent operational costs to business value metrics such as task completion rates, time saved, and overall cost efficiency. This direct ROI visualization in portals and APIs equips stakeholders to make informed investment decisions and prioritize improvements.

How It Works

1. Unified Tracing Across Frameworks

Agents produce telemetry traces via OpenTelemetry, capturing every step—prompt issuance, LLM call, tool invocation, and sub-agent hops. Foundry collects these spans regardless of which SDK or framework generates them, stitching together unified execution traces. Developers simply connect their OTel exporters to Foundry to enable this continuity.

const otelExporter = new OpenTelemetryExporter({
  endpoint: "https://foundry.azure.com/otel",
  apiKey: process.env.FOUNDRY_API_KEY,
});

agent.setTelemetryExporter(otelExporter);

This simplifies heterogeneous agent observability by leveraging industry standards instead of proprietary SDK instrumentation.

2. Multi-Turn Evaluation & Rubric Scoring

Before agents reach production, Foundry’s user simulation creates realistic multi-turn conversations, including edge-case scenarios, to exercise agent logic thoroughly. The multi-turn evaluation measures consistency of reasoning, tone, and task completion over the entire interaction, rather than isolated requests.

The rubric evaluator builds task-specific quality criteria on top of these traces:

• It incorporates weighted factors like safety, latency, cost, and tone.
• It can be customized per use case.
• Evaluations run both pre-deploy and continuously in production.

This method catches subtle regressions and enforces enterprise-grade trust requirements.

3. Developer-Focused Observability Experience

All trace and evaluation data surfaces directly inside azd and VS Code. After deploying an agent, devs can view real-time telemetry and evaluation scores inline without shifting context.

Screen capture illustrating the integrated Azure Developer CLI experience. Source: Microsoft Foundry Blog

4. Intelligent Trace Sampling & Incident Replay

To control evaluation costs, Foundry uses intelligent trace sampling that selects signal-rich traces for detailed evaluation rather than exhaustively processing every request. This balances continuous quality monitoring with operational cost-efficiency.

When a regression is detected, developers can replay exact execution traces visually and diagnostically, enabling root cause analysis without reproducing errors manually.

5. Continuous Optimization Loop with Agent Optimizer

The Agent Optimizer analyzes current agent prompts, skills, and configurations; proposes evidence-ranked changes; and evaluates candidate variants against the rubric:

• Side-by-side diff views show what improved or regressed.
• Fully audit-trailed rollback ensures safe deployments.
• New production traces feed automated re-evaluation, closing the loop.

This moves agent improvement from guesswork to a governed continuous process.

6. ROI Measurement & Business Value Dashboard

Integrating telemetry and evaluative data, Foundry calculates business KPIs like task completion rates, time saved, and operational cost versus value:

Example of Foundry Agent ROI dashboard. Source: Microsoft Foundry Blog

This data-driven approach helps justify AI investments, prioritize agent versions, and guide strategic development.

Quick Tips & Tricks

1. Leverage OpenTelemetry for Instant Cross-Framework Observability
   If your agents already emit OpenTelemetry spans, integrate them with Foundry’s tracing endpoint for immediate observability without reengineering your pipeline.

2. Use Multi-Turn Evaluations to Catch Hidden Failures
   Run realistic, automatically generated multi-turn conversations via Foundry’s user simulation to identify issues like tone drift and progressive goal loss that single-turn tests overlook.

3. Customize Rubric Evaluations to Match Business Objectives
   Tailor rubric weighting and evaluation criteria to your agent’s specific context—whether compliance, customer support, or sales automation—to get meaningful quality scores.

4. Embed Observability in Developer Workflows with Azure Developer CLI
   Enable the observability features in azd to troubleshoot failures, view evaluation results, and iterate on agent logic without leaving your code editor or terminal.

5. Incorporate Intelligent Trace Sampling to Manage Costs
   Avoid evaluating every single production request by using Foundry’s intelligent sampling, so you monitor quality continuously yet cost-effectively.

6. Use Agent Optimizer for Data-Driven Improvements
   Adopt the Agent Optimizer to transform guess-and-check tuning into a governed, automated improvement loop with clear audit trails and rollback safety nets.

Conclusion

Microsoft Foundry’s latest innovations at Build 2026 redefine AI agent lifecycle management by providing continuous, cross-framework observability paired with rigorous evaluation and optimization. This holistic platform empowers developer teams to maintain trustworthiness, safety, and quality for AI agents that evolve dynamically in production environments. Importantly, it closes the loop from detailed telemetry to actionable improvements and finally to demonstrable business ROI.

As AI agents become core to enterprise digital transformation, the ability to observe, optimize, and prove value continuously will separate successful deployments from costly, opaque AI projects. With Foundry’s integrated tooling—backed by Azure Monitor and a developer-centric experience—teams now can confidently build AI agents at scale, accelerate time to value, and align AI efforts with measurable business outcomes.

References

1. Build 2026: From observability to ROI for AI agents on any framework | Microsoft Foundry Blog — Official Microsoft Foundry announcement covering agent lifecycle observability and optimization.
2. Microsoft Foundry Observability Documentation — Detailed documentation on Foundry tracing, evaluation, and monitoring features.
3. Azure Developer CLI (azd) Official Docs — Integration points for developer observability experiences.
4. OpenTelemetry Specification — Understanding the open standard enabling cross-framework telemetry.
5. Azure Monitor Overview — Monitoring, alerting, and dashboard service integrated into Foundry’s observability.