From Semantic Kernel to Agent Framework 1.0: A Practical Migration Playbook for .NET and Python Teams

Agentic AI has officially moved from experiment to platform strategy. In early April 2026, Microsoft announced Microsoft Agent Framework 1.0 for both .NET and Python, while the ecosystem around agent tooling accelerated with AG-UI demos, long-running background response patterns, and a visible jump in open-source agent repos on GitHub trending.

If you built prototypes with Semantic Kernel or AutoGen in 2024/2025, this is the moment to harden your architecture for production.

In this guide, I’ll give you a practical migration playbook you can apply this week: how to move to Agent Framework 1.0 safely, where .NET and Python teams should divide responsibilities, and what to do to avoid the common migration traps.

Why this matters now

• Framework maturity: Agent Framework reached 1.0 with stable APIs and long-term support commitments.
• Cross-language parity: .NET and Python now have a clearer shared foundation for orchestration.
• Ops patterns are improving: Microsoft’s recent guidance on background responses and approval gates reflects real production pain points being solved.
• Market pull: Weekly GitHub trends show strong momentum for agent-native projects, meaning expectations from stakeholders are rising faster than before.

In short: your architecture choices now will either reduce future refactoring—or multiply it.

The migration mindset: don’t port, re-baseline

Most teams start migration with how do I map old class X to new class Y? That mindset is too narrow.

Instead, treat the move as a platform re-baseline:

1. Define your agent boundaries (what each agent owns and what it must never do).
2. Define your execution contract (tool access, approval requirements, timeouts, retry policy).
3. Define your handoff contract between agents and runtimes (.NET↔Python).
4. Define your operability baseline (logs, traces, run IDs, cost tracking, failure modes).

Then map old code into this model. Not the other way around.

A practical target architecture for mixed .NET + Python teams

For teams running both stacks, this split tends to work well:

• .NET for API surface, identity, policy enforcement, and enterprise integration.
• Python for rapid tool experimentation, data workflows, and model-adjacent pipelines.
• Agent Framework as the orchestration layer and shared contract.

Suggested composition

• Coordinator agent (usually .NET): receives user/system goal, enforces policy, selects specialist agents.
• Specialist agents (.NET or Python): domain tools (search, document analysis, coding, data transforms).
• Approval gateway: human-in-the-loop for risky actions (writes, external messaging, irreversible operations).
• Background runner: long tasks detached from request/response path.
• Observation layer: per-step telemetry and execution transcript.

This mirrors where the ecosystem is going: reliable, observable, approval-aware agent workflows—not single giant prompt systems.

Migration checklist (the version you can run this sprint)

1) Inventory your current agent surface

Create a one-page inventory:

• Agents in production and their goals
• Tools each agent can call
• High-risk actions (file writes, external APIs, message sending)
• Current timeout / retry behavior
• Known failure patterns

If this inventory does not exist, migration risk is already high.

2) Standardize tool contracts before framework migration

Many migrations fail because tools are inconsistent, not because the framework changed.

Make tool outputs predictable:

• Return structured JSON with explicit success/failure states.
• Include machine-readable error codes.
• Add deterministic idempotency keys for write operations.
• Record correlation IDs for every tool call.

When you do this first, framework migration gets much easier.

3) Introduce approval policies explicitly

Recent Agent Framework content emphasizes approval controls for script/tool execution. That’s not a nice to have. It’s table stakes.

Define policy tiers:

• Auto-allow: read-only tools and low-impact actions.
• Require approval: external side effects, financial actions, data mutation.
• Deny by default: operations outside declared scope.

Then codify this as policy metadata, not prompt text.

4) Move long-running tasks to background responses

Any workflow that can exceed normal HTTP request windows should run in background mode. Typical examples:

• Deep research
• Large multi-file transformations
• Long content generation + validation loops
• Cross-system orchestration with external dependencies

Pattern to implement:

1. Start run and return run ID immediately.
2. Persist progress checkpoints.
3. Stream status updates to UI or queue.
4. Allow cancellation and safe resume.

Do this and your product feels stable even when models take longer to think.

5) Build a real multi-agent UI, not a hidden workflow

The AG-UI + Agent Framework demo highlights a key lesson: users need to understand what agent is active, why execution is blocked, and what approval is needed.

At minimum, expose:

• Current active agent
• Current task step
• Pending approval requests
• Last tool call and result summary
• Final artifacts and confidence notes

Trust increases dramatically when workflow state is visible.

Common migration traps (and fixes)

Trap 1: Keeping prompt logic as your control plane

Fix: Move permissions, retry limits, and escalation paths into code-level policy and orchestration config.

Trap 2: Treating .NET and Python as separate products

Fix: Use shared schemas and event envelopes. Language-specific implementations are fine; protocol drift is not.

Trap 3: No SLA for tool calls

Fix: Set latency/error budgets per tool class and fail fast with fallback plans.

Trap 4: No postmortem discipline for agent failures

Fix: Log every run with replay context and conduct weekly failure reviews.

90-day rollout plan

Days 1–30: Foundation

• Inventory current agents and tools
• Define policy tiers and approval matrix
• Create shared contract schemas
• Pick one low-risk workflow as pilot

Days 31–60: Pilot and hardening

• Migrate pilot workflow to Agent Framework 1.0
• Add background response path
• Implement run-level observability dashboard
• Run failure injection tests

Days 61–90: Scale

• Migrate 2–3 business-critical workflows
• Standardize reusable agent skill packages
• Publish internal migration template for all teams
• Track KPI deltas (resolution time, failure rate, human escalations)

Final take

Agentic systems are entering their engineering discipline phase. The winners won’t be the teams with the fanciest demos—they’ll be the ones that operationalize orchestration, approvals, visibility, and cross-language consistency.

Agent Framework 1.0 arrives at the right moment for that shift.

If your team is still running prototype-era patterns, now is a good week to move.