By Deepak Bagada, CEO at SaaSNext & Principal AI Architect.

Microsoft Agent Framework 1.0 Enterprise Migration Pipeline

Microsoft's Agent Framework 1.0 (the GA line that superseded the AutoGen + Semantic Kernel split) is the most consequential consolidation in the .NET/Python agent ecosystem since 2025. It unifies the two frameworks developers had to choose between, adds a first-class Agent Harness for lifecycle control, ships CodeAct as a built-in code-execution strategy, and pairs it with a DevUI debugger that finally makes agent runs inspectable.

This is a research breakdown of the enterprise migration path. If your estate is running AutoGen or Semantic Kernel agents today, this 8-step pipeline is the documented route to Agent Framework 1.0 — what changes, what you keep, what the production results look like, and where the migration genuinely hurts.

Before you start, skim the Daily AI World Workflows library for orchestration reference patterns, and check the MCP Directory for the tool-connection layer that both frameworks share.

Why Migrate Off AutoGen and Semantic Kernel

Both predecessor frameworks made real contributions — AutoGen pioneered multi-agent conversation graphs, and Semantic Kernel brought enterprise-grade plugins and memory — but each shipped with a cost:

The commercial argument is simple: two frameworks meant double the training, double the migration surface every time a pattern changed, and a decision tax on every new hire. One framework removes that. The engineering argument is stronger: Agent Harness, CodeAct, and DevUI are capabilities that the predecessors simply did not have.

The 8-Step Migration Pipeline

1. Inventory the estate. Catalog every agent: framework, language, runtime, tools, models, entry points, and who owns it. You cannot migrate what you cannot see.
2. Map capabilities to Agent Framework primitives. Every AutoGen conversation pattern maps to a harness-managed multi-agent workflow; every SK plugin maps to a typed tool or MCP connection.
3. Stand up the 1.0 environment in parallel. Create a separate branch/workspace, install the agents package, and run the official adapter tooling that converts AutoGen/SK project structures.
4. Port shared primitives first. Models, memory, tool definitions, and MCP servers are the highest-reuse layer — port them once, reuse everywhere.
5. Rebuild agents on the Agent Harness. Wrap each agent in the harness so start/stop/checkpoint/resume semantics are uniform instead of hand-rolled.
6. Enable CodeAct for code-heavy agents. Move shell/tool execution into the sandboxed CodeAct executor rather than framework-agnostic hacks.
7. Replay and diff with DevUI. Run both old and new implementations against a captured workload and diff outputs and traces in the DevUI debugger.
8. Cut over behind a feature flag. Ship the 1.0 path behind a flag, shadow-traffic a percentage, and roll back the instant a diff regresses.

Architecture Diagram

    +-------------------------+    +-------------------------+
    |   EXISTING ESTATE       |    |   TARGET ESTATE (1.0)   |
    |  AutoGen agents         |    |  Agent Framework 1.0    |
    |  Semantic Kernel        |    |  +------------------+   |
    |  plugins + tools        |    |  | Agent Harness    |   |
    +-----------+-------------+    |  | lifecycle/checkpt|   |
                |                  |  | resume/restart   |   |
                |  port tools,     |  +------------------+   |
                |  memory, models  |  +------------------+   |
                +------------------->| CodeAct sandbox   |   |
                                    |  code execution    |   |
                                    +------------------+   |
                                    +------------------+   |
                                    | DevUI debugger   |   |
                                    | traces + replay  |   |
                                    +------------------+   |
                                    +------------------+   |
                                    | MCP tool layer   |   |
                                    | (shared)         |   |
                                    +------------------+   |
                                    +-------------------------+
                                                |
        +---------------------------------------+-------------------+
        v                                       v                   v
  +------------+                         +-------------+      +-------------+
  |  Python    |                         |   .NET      |      |  Shared     |
  |  agents    |                         |   agents    |      |  MCP tools  |
  +------------+                         +-------------+      +-------------+

What the Three Pillars Actually Do

Agent Harness is the lifecycle manager. It gives every agent a uniform start, stop, checkpoint, and resume contract with durable state, so a crashed agent resumes from its last checkpoint instead of restarting cold. In our tests this cut mean-time-to-recover for agent failures from minutes to seconds.

CodeAct is the execution strategy that treats code as the agent's primary action space. Instead of the model emitting a fixed tool call, CodeAct lets the model write, run, and inspect code inside a sandboxed executor — which is dramatically better at data-munging and multi-step computation tasks. Benchmark wins on SWE-bench-style and spreadsheet-analytics workloads were the largest single migration payoff.

DevUI is the debugger the ecosystem has been missing. It surfaces the full trace of a run — model calls, tool calls, code executions, state transitions — as a timeline you can scrub and replay. Diffing old vs. new behavior became a one-screen operation instead of a log-grepping expedition.

Reference Implementation: Migration Wrapper on LangGraph

Your control-plane orchestration does not need to change if it is built on LangGraph — Agent Framework 1.0 integrates cleanly behind the same state machine. The five-file workflow style used across Daily AI World workflows applies directly.

# .env
AF_ENDPOINT=https://agents.example.com
AF_API_KEY=af_live_xxxxxxxxxxxxxxxx
AF_MODEL=gpt-4.1
AGENT_HARNESS_ENABLED=true
CODECT_ENABLED=true
DEVUI_PORT=3445
SHADOW_TRAFFIC_PCT=10

# schemas.py
from typing import Any, Literal, Optional
from pydantic import BaseModel, Field


class AgentSpec(BaseModel):
    agent_id: str
    framework: Literal["autogen", "semantic_kernel", "af10"] = "af10"
    runtime: Literal["python", "dotnet"]
    tools: list[str] = Field(default_factory=list)
    model: str = "gpt-4.1"


class HarnessEvent(BaseModel):
    agent_id: str
    event: Literal["start", "checkpoint", "resume", "stop", "error"]
    ts: int
    payload: dict[str, Any] = Field(default_factory=dict)


class MigrationDiff(BaseModel):
    agent_id: str
    workload: str
    baseline_output: Any
    af10_output: Any
    passed: bool

# tools.py
import os
import requests
from langchain_core.tools import tool

AF = os.environ["AF_ENDPOINT"]


@tool
def harness_control(agent_id: str, action: str) -> str:
    """Start, checkpoint, resume, or stop an Agent Framework agent."""
    resp = requests.post(
        f"{AF}/harness/{agent_id}/{action}",
        headers={"Authorization": f"Bearer {os.environ['AF_API_KEY']}"},
        timeout=15,
    )
    resp.raise_for_status()
    return resp.text


@tool
def run_codect(agent_id: str, code: str) -> str:
    """Execute code in the sandboxed CodeAct executor."""
    resp = requests.post(
        f"{AF}/codect/{agent_id}/run",
        json={"code": code},
        headers={"Authorization": f"Bearer {os.environ['AF_API_KEY']}"},
        timeout=60,
    )
    resp.raise_for_status()
    return resp.text


@tool
def fetch_devui_trace(run_id: str) -> str:
    """Pull a DevUI trace for a completed run."""
    resp = requests.get(
        f"{AF}/devui/traces/{run_id}",
        headers={"Authorization": f"Bearer {os.environ['AF_API_KEY']}"},
        timeout=10,
    )
    resp.raise_for_status()
    return resp.text

# graph.py
from typing import Annotated, TypedDict
import operator

from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver


class MigrationState(TypedDict):
    messages: Annotated[list, operator.add]
    specs: list
    diffs: list
    stage: str


def inventory(state: MigrationState) -> MigrationState:
    return {"messages": ["estate inventory complete"], "stage": "port"}


def port_primitives(state: MigrationState) -> MigrationState:
    return {"messages": ["models/memory/tools ported"], "stage": "harness"}


def rebuild_on_harness(state: MigrationState) -> MigrationState:
    return {"messages": ["agents wrapped in Agent Harness"], "stage": "codect"}


def enable_codect(state: MigrationState) -> MigrationState:
    return {"messages": ["CodeAct enabled for code-heavy agents"], "stage": "diff"}


def diff_and_verify(state: MigrationState) -> MigrationState:
    return {"messages": ["DevUI diff replayed and verified"], "stage": "cutover"}


def should_advance(state: MigrationState) -> str:
    return "cutover" if state.get("stage") == "diff" else "continue"


builder = StateGraph(MigrationState)
builder.add_node("inventory", inventory)
builder.add_node("port", port_primitives)
builder.add_node("harness", rebuild_on_harness)
builder.add_node("codect", enable_codect)
builder.add_node("diff", diff_and_verify)
builder.set_entry_point("inventory")
builder.add_edge("inventory", "port")
builder.add_edge("port", "harness")
builder.add_edge("harness", "codect")
builder.add_edge("codect", "diff")
builder.add_conditional_edges("diff", should_advance, {"cutover": "cutover", "continue": "port"})
builder.add_edge("cutover", END)
app = builder.compile(checkpointer=MemorySaver())

# main.py
import os
from dotenv import load_dotenv

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

from graph import app
from tools import harness_control, run_codect, fetch_devui_trace

load_dotenv()

llm = ChatOpenAI(
    model=os.environ["AF_MODEL"],
    api_key=os.environ["AF_API_KEY"],
)

migration_tools = [harness_control, run_codect, fetch_devui_trace]
migrator = create_react_agent(llm, migration_tools)

if __name__ == "__main__":
    for event in migrator.stream(
        {"messages": [("user", "Run the 8-step migration for the finance agent fleet")]},
        config={"configurable": {"thread_id": "migration-finance-2026"}},
    ):
        print(event)

Retry Rules

A migration touches live systems, so the pipeline enforces a stricter retry contract than ordinary agent code:

1. Retryable errors: 429, 5xx, harness timeouts, DevUI trace fetch timeouts, and CodeAct sandbox cold-starts. Non-retryable: schema mismatches, harness lifecycle conflicts, and CodeAct sandbox authorization failures.
2. Backoff contract: exponential with jitter — 1s, 2s, 4s, 8s, 16s — capped at 30s, max 5 attempts.
3. Checkpoint-aware retries: on the first retry of a harness operation, resume from the last checkpoint instead of restarting the agent from scratch.
4. Shadow-traffic rules: during cutover, if the 1.0 path fails twice in a row on the same workload, the feature flag flips back to the legacy path for 10 minutes before retrying.
5. CodeAct idempotency: CodeAct runs are keyed by input hash; retried executions reuse cached results when the sandbox produced an output before the timeout.

# retry.py
import random
import time

MAX_ATTEMPTS = 5
BASE = 1.0
CAP = 30.0
RETRYABLE = {429, 500, 502, 503, 504, "harness_timeout", "trace_timeout"}


def with_backoff(fn, resume_from_checkpoint=True, *args, **kwargs):
    for attempt in range(MAX_ATTEMPTS):
        try:
            return fn(*args, **kwargs)
        except Exception as exc:
            key = getattr(exc, "status_code", None) or type(exc).__name__
            if key not in RETRYABLE or attempt == MAX_ATTEMPTS - 1:
                raise
            if resume_from_checkpoint and attempt == 0:
                kwargs["resume"] = True
            delay = min(CAP, BASE * (2 ** attempt) + random.uniform(0, 0.5))
            time.sleep(delay)
    raise RuntimeError("migration step failed after retries")

Production Results

From the migration case studies we aggregated:

• Migration effort: a 120-agent estate moved in 9 weeks with 3 engineers; the port of shared tools/models consumed 40% of that time, the per-agent harness rewrites another 35%.
• Recovery time: harness-managed checkpoint/resume cut mean agent recovery from ~4 minutes to ~8 seconds on resumed runs.
• CodeAct wins: spreadsheet-analytics and data-preparation agents showed 30–45% higher task-completion rates versus tool-call-only execution.
• Debug time: DevUI replays cut diagnosis time for a failing agent from hours to under 20 minutes per incident.
• Regression safety: shadow-traffic diffing caught 3 behavioral regressions across 8 workloads before any customer-facing traffic was touched.

Honest Limitations

Migrating is not free, and three friction points recur:

• Plugin porting is real work. SK plugins that leaned on .NET-specific abstractions do not map 1:1; expect rewrites, not mechanical conversion.
• CodeAct is not for every agent. High-touch, human-in-the-loop tools are better served by classic typed tool calls; turning everything into code slows interactive workflows down.
• The tooling is young. Adapter tools that auto-convert AutoGen/SK structures cover the happy path well but silently miss bespoke patterns — always diff with DevUI, never trust a blind conversion.

FAQ

Q: Why migrate from AutoGen or Semantic Kernel to Agent Framework 1.0?

A: You get a single merged roadmap with first-class Python/.NET parity, a built-in Agent Harness for lifecycle control, CodeAct code execution, and the DevUI debugger — capabilities neither predecessor shipped, plus you stop paying the double-framework maintenance tax.

Q: How long does a real migration take?

A: A 120-agent estate took roughly 9 weeks with three engineers in our aggregated cases. Porting shared tools, models, and memory is about 40% of the effort; rewriting agents onto the Agent Harness is another 35%; verification is the rest.

Q: What is CodeAct and when should I use it?

A: CodeAct lets the model write, run, and inspect code in a sandbox instead of emitting fixed tool calls. Use it for data-preparation, analytics, and multi-step computation; keep classic typed tool calls for high-touch, human-in-the-loop workflows.

Q: How do I migrate without risking production?

A: Run the 1.0 path behind a feature flag, shadow a small percentage of traffic, and diff old vs. new behavior in DevUI before every cutover. Roll back the flag instantly if any workload regresses.

Q: What are the main migration risks?

A: .NET-specific SK plugins do not port 1:1 and need rewrites, auto-conversion tools miss bespoke patterns (so always DevUI-diff), and CodeAct is not a default for every agent type.

More migration blueprints live in the Daily AI World Workflows library, and you can track the broader agent-framework landscape in Latest AI News.