Coding After Coders: The End of Computer Programming as We Know It

I think the piece accurately and clearly captures what's going on in our industry right now in terms appropriate for a wider audience.

I talked to Clive a few weeks ago. Here's the quote from me that made it into the piece.

Given A.I.’s penchant to hallucinate, it might seem reckless to let agents push code out into the real world. But software developers point out that coding has a unique quality: They can tether their A.I.s to reality, because they can demand the agents test the code to see if it runs correctly. “I feel like programmers have it easy,” says Simon Willison, a tech entrepreneur and an influential blogger about how to code using A.I. “If you’re a lawyer, you’re screwed, right?” There’s no way to automatically check a legal brief written by A.I. for hallucinations — other than face total humiliation in court.

The piece does raise the question of what this means for the future of our chosen line of work, but the general attitude from the developers interviewed was optimistic - there's even a mention of the possibility that the Jevons paradox might increase demand overall.

One critical voice came from an Apple engineer:

A few programmers did say that they lamented the demise of hand-crafting their work. “I believe that it can be fun and fulfilling and engaging, and having the computer do it for you strips you of that,” one Apple engineer told me. (He asked to remain unnamed so he wouldn’t get in trouble for criticizing Apple’s embrace of A.I.)

That request to remain anonymous is a sharp reminder that corporate dynamics may be suppressing an unknown number of voices on this topic.

Tags: new-york-times, careers, ai, generative-ai, llms, ai-assisted-programming, press-quotes, deep-blue

Agent harness is the layer where model reasoning connects to real execution: shell and filesystem access, approval flows, and context management across long-running sessions. With Agent Framework, these patterns can now be built consistently in both Python and .NET.

In this post, we’ll look at three practical building blocks for production agents:

• Local shell harness for controlled host-side execution
• Hosted shell harness for managed execution environments
• Context compaction for keeping long conversations efficient and reliable

Shell and Filesystem Harness

Many agent experiences need to do more than generate text. They need to inspect files, run commands, and work with the surrounding environment in a controlled way. Agent Framework makes it possible to model those capabilities explicitly, with approval patterns where needed.

The following examples show compact harness patterns in both Python and .NET.

Python: Local shell with approvals

import asyncio
import subprocess
from typing import Any

from agent_framework import Agent, Message, tool
from agent_framework.openai import OpenAIResponsesClient


@tool(approval_mode="always_require")
def run_bash(command: str) -> str:
    """Execute a shell command locally and return stdout, stderr, and exit code."""
    result = subprocess.run(
        command,
        shell=True,
        capture_output=True,
        text=True,
        timeout=30,
    )
    parts: list[str] = []
    if result.stdout:
        parts.append(result.stdout)
    if result.stderr:
        parts.append(f"stderr: {result.stderr}")
    parts.append(f"exit_code: {result.returncode}")
    return "\n".join(parts)


async def run_with_approvals(query: str, agent: Agent) -> Any:
    current_input: str | list[Any] = query

    while True:
        result = await agent.run(current_input)
        if not result.user_input_requests:
            return result

        next_input: list[Any] = [query]
        for request in result.user_input_requests:
            print(f"Shell request: {request.function_call.name}")
            print(f"Arguments: {request.function_call.arguments}")
            approved = (await asyncio.to_thread(input, "Approve command? (y/n): ")).strip().lower() == "y"
            next_input.append(Message("assistant", [request]))
            next_input.append(Message("user", [request.to_function_approval_response(approved)]))
            if not approved:
                return "Shell command execution was rejected by user."

        current_input = next_input


async def main() -> None:
    client = OpenAIResponsesClient(
        model_id="<responses-model-id>",
        api_key="<your-openai-api-key>",
    )
    local_shell_tool = client.get_shell_tool(func=run_bash)

    agent = Agent(
        client=client,
        instructions="You are a helpful assistant that can run shell commands.",
        tools=[local_shell_tool],
    )

    result = await run_with_approvals(
        "Use run_bash to execute `python --version` and show only stdout.",
        agent,
    )
    print(result)


if __name__ == "__main__":
    asyncio.run(main())

This pattern keeps execution on the host machine while giving the application a clear approval checkpoint before the command runs.

Security note: For local shell execution, we recommend running this logic in an isolated environment and keeping explicit approval in place before commands are allowed to run.

Python: Hosted shell in a managed environment

import asyncio

from agent_framework import Agent
from agent_framework.openai import OpenAIResponsesClient


async def main() -> None:
    client = OpenAIResponsesClient(
        model_id="<responses-model-id>",
        api_key="<your-openai-api-key>",
    )
    shell_tool = client.get_shell_tool()

    agent = Agent(
        client=client,
        instructions="You are a helpful assistant that can execute shell commands.",
        tools=shell_tool,
    )

    result = await agent.run("Use a shell command to show the current date and time")
    print(result)

    for message in result.messages:
        shell_calls = [c for c in message.contents if c.type == "shell_tool_call"]
        shell_results = [c for c in message.contents if c.type == "shell_tool_result"]

        if shell_calls:
            print(f"Shell commands: {shell_calls[0].commands}")
        if shell_results and shell_results[0].outputs:
            for output in shell_results[0].outputs:
                if output.stdout:
                    print(f"Stdout: {output.stdout}")
                if output.stderr:
                    print(f"Stderr: {output.stderr}")
                if output.exit_code is not None:
                    print(f"Exit code: {output.exit_code}")


if __name__ == "__main__":
    asyncio.run(main())

Hosted shell is useful when you want the agent to execute commands in a provider-managed environment rather than directly on the local machine.

.NET: Local shell with approvals

using System.ComponentModel;
using System.Diagnostics;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI;

var apiKey = "<your-openai-api-key>";
var model = "<responses-model-id>";

[Description("Execute a shell command locally and return stdout, stderr and exit code.")]
static string RunBash([Description("Bash command to execute.")] string command)
{
    using Process process = new()
    {
        StartInfo = new ProcessStartInfo
        {
            FileName = "/bin/bash",
            ArgumentList = { "-lc", command },
            RedirectStandardOutput = true,
            RedirectStandardError = true,
            UseShellExecute = false,
        }
    };

    process.Start();
    process.WaitForExit(30_000);

    string stdout = process.StandardOutput.ReadToEnd();
    string stderr = process.StandardError.ReadToEnd();

    return $"stdout:\n{stdout}\nstderr:\n{stderr}\nexit_code:{process.ExitCode}";
}

IChatClient chatClient = new OpenAIClient(apiKey)
    .GetResponsesClient(model)
    .AsIChatClient();

AIAgent agent = chatClient.AsAIAgent(
    name: "LocalShellAgent",
    instructions: "Use tools when needed. Avoid destructive commands.",
    tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(RunBash, name: "run_bash"))]);

AgentSession session = await agent.CreateSessionAsync();
AgentResponse response = await agent.RunAsync("Use run_bash to execute `dotnet --version` and return only stdout.", session);

List<FunctionApprovalRequestContent> approvalRequests = response.Messages
    .SelectMany(m => m.Contents)
    .OfType<FunctionApprovalRequestContent>()
    .ToList();

while (approvalRequests.Count > 0)
{
    List<ChatMessage> approvals = approvalRequests
        .Select(request => new ChatMessage(ChatRole.User, [request.CreateResponse(approved: true)]))
        .ToList();

    response = await agent.RunAsync(approvals, session);
    approvalRequests = response.Messages
        .SelectMany(m => m.Contents)
        .OfType<FunctionApprovalRequestContent>()
        .ToList();
}

Console.WriteLine(response);

Like the Python version, this approach combines local execution with an explicit approval flow so the application stays in control of what actually runs.

Security note: For local shell execution, we recommend running this logic in an isolated environment and keeping explicit approval in place before commands are allowed to run.

.NET: Hosted shell with protocol-level configuration

using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using OpenAI;
using OpenAI.Responses;

var apiKey = "<your-openai-api-key>";
var model = "<responses-model-id>";

IChatClient chatClient = new OpenAIClient(apiKey)
    .GetResponsesClient(model)
    .AsIChatClient();

CreateResponseOptions hostedShellOptions = new();
hostedShellOptions.Patch.Set(
    "$.tools"u8,
    BinaryData.FromObjectAsJson(new object[]
    {
        new
        {
            type = "shell",
            environment = new
            {
                type = "container_auto"
            }
        }
    }));

AIAgent agent = chatClient
    .AsBuilder()
    .BuildAIAgent(new ChatClientAgentOptions
    {
        Name = "HostedShellAgent",
        UseProvidedChatClientAsIs = true,
        ChatOptions = new ChatOptions
        {
            Instructions = "Use shell commands to answer precisely.",
            RawRepresentationFactory = _ => hostedShellOptions
        }
    });

AgentResponse response = await agent.RunAsync("Use a shell command to print UTC date/time. Return only command output.");
Console.WriteLine(response);

This makes it possible to target a managed shell environment from .NET today while keeping the rest of the agent flow in the standard Agent Framework programming model.

Context Compaction

Long-running agent sessions accumulate chat history that can exceed a model’s context window. The Agent Framework includes a built-in compaction system that automatically manages conversation history before each model call — keeping agents within their token budget without losing important context (Docs).

Python: In-run compaction on the agent

import asyncio

from agent_framework import Agent, InMemoryHistoryProvider, SlidingWindowStrategy, tool
from agent_framework.openai import OpenAIChatClient


@tool(approval_mode="never_require")
def get_weather(city: str) -> str:
    weather_data = {
        "London": "cloudy, 12°C",
        "Paris": "sunny, 18°C",
        "Tokyo": "rainy, 22°C",
    }
    return weather_data.get(city, f"No data for {city}")


async def main() -> None:
    client = OpenAIChatClient(
        model_id="<chat-model-id>",
        api_key="<your-openai-api-key>",
    )

    agent = Agent(
        client=client,
        instructions="You are a helpful weather assistant.",
        tools=[get_weather],
        context_providers=[InMemoryHistoryProvider()],
        compaction_strategy=SlidingWindowStrategy(keep_last_groups=3),
    )

    session = agent.create_session()
    for query in [
        "What is the weather in London?",
        "How about Paris?",
        "And Tokyo?",
        "Which city is the warmest?",
    ]:
        result = await agent.run(query, session=session)
        print(result.text)


if __name__ == "__main__":
    asyncio.run(main())

This example keeps the most recent conversational context intact while trimming older tool-heavy exchanges that no longer need to be replayed in full.

.NET: Compaction pipeline with multiple strategies

using System.ComponentModel;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Compaction;
using Microsoft.Extensions.AI;
using OpenAI;

var apiKey = "<your-openai-api-key>";
var model = "<chat-model-id>";

[Description("Look up the current price of a product by name.")]
static string LookupPrice([Description("The product to look up.")] string productName) =>
    productName.ToUpperInvariant() switch
    {
        "LAPTOP" => "The laptop costs $999.99.",
        "KEYBOARD" => "The keyboard costs $79.99.",
        "MOUSE" => "The mouse costs $29.99.",
        _ => $"No data for {productName}."
    };

IChatClient chatClient = new OpenAIClient(apiKey)
    .GetChatClient(model)
    .AsIChatClient();

PipelineCompactionStrategy compactionPipeline = new(
    new ToolResultCompactionStrategy(CompactionTriggers.MessagesExceed(7)),
    new SlidingWindowCompactionStrategy(CompactionTriggers.TurnsExceed(4)),
    new TruncationCompactionStrategy(CompactionTriggers.GroupsExceed(12)));

AIAgent agent = chatClient
    .AsBuilder()
    .UseAIContextProviders(new CompactionProvider(compactionPipeline))
    .BuildAIAgent(new ChatClientAgentOptions
    {
        Name = "ShoppingAssistant",
        ChatOptions = new ChatOptions
        {
            Instructions = "You are a concise shopping assistant.",
            Tools = [AIFunctionFactory.Create(LookupPrice)]
        },
        ChatHistoryProvider = new InMemoryChatHistoryProvider()
    });

AgentSession session = await agent.CreateSessionAsync();

string[] prompts =
[
    "What's the price of a laptop?",
    "How about a keyboard?",
    "And a mouse?",
    "Which is cheapest?",
    "What was the first product I asked about?"
];

foreach (string prompt in prompts)
{
    Console.WriteLine($"User: {prompt}");
    AgentResponse response = await agent.RunAsync(prompt, session);
    Console.WriteLine($"Agent: {response}\\n");

    if (session.TryGetInMemoryChatHistory(out var history))
    {
        Console.WriteLine($"[Stored message count: {history.Count}]\\n");
    }
}

By combining multiple compaction strategies, you can keep sessions responsive and cost-aware without giving up continuity.

What’s Next?

These patterns make Agent Framework a stronger foundation for real-world agent systems:

• Local shell with approvals enables controlled execution on the host.
• Hosted shell supports execution in managed environments.
• Compaction strategies help long-running sessions stay within limits while preserving useful context.

Whether you are building an assistant that can inspect a project workspace or a multi-step workflow that needs durable context over time, these capabilities help close the gap between model reasoning and practical execution.

For more information, check out our documentation and examples on GitHub, and install the latest packages from NuGet (.NET) or PyPI (Python).

The post Agent Harness in Agent Framework appeared first on Microsoft Agent Framework.

TL;DR; No - AI won’t kill open source, but it will reshape it. Small, single-purpose packages (micro open source) are likely to languish as AI agents write trivial utility code on the fly. But major frameworks, databases, and runtimes like Django, Postgres, and Python itself aren’t going anywhere - AI agents actually prefer reaching for established building blocks over reinventing them. The key is staying in the architect’s seat.

AI will replace the trivial, leave the foundational, and force us to rethink everything in between:

• Micro open source (utility packages): Likely to decline – AI writes trivial code faster than importing it
• Mid-level libraries: Case-by-case – depends on complexity and maintenance burden
• Major frameworks and infrastructure: Safe – AI agents prefer uv pip install over reinventing Django

I sat down with Paul Everitt to debate this question, and it turns out the answer is way more nuanced than a simple yes or no.

Watch the full conversation on YouTube →

Why would AI rebuild what frameworks already provide?

Paul kicked things off with a great framing. Think of building an app like a 100-meter soccer field. A framework like Flask or Django gets you 95 meters down the field. You and your AI agent only need to handle the last 5 meters – the part that’s unique to your app.

Why would an agent rebuild those 95 meters from scratch when it can just uv pip install the framework and focus on the hard part? Software is a liability, not an asset, and owning all of that code means owning all of those future bugs.

But there’s a counterargument: if you only need 10% of a framework, you’re still dragging in the other 90% – attack surface, security issues, maintenance burden. Maybe you’re better off owning a small thing than renting a large one?

Will AI replace small open source packages?

I think the real casualty here is micro open source – those tiny packages that wrap a single function or a handful of utility classes.

Evidence? Tailwind usage: It’s up 600% in the last 18 months, largely because AI loves reaching for it. But the revenue story for Tailwind is heading in the opposite direction. AI can easily write the 47 utility classes you actually need instead of pulling in the whole framework.

What should go? Micro-packages: There’s the left-pad cautionary tale. A single trivial function as a standalone package took down huge swaths of the JavaScript ecosystem when its maintainer pulled it. AI should absolutely be writing those two functions for us instead of importing a package for them.

Can AI replace major frameworks like Django or Postgres?

Here’s what I don’t see happening: an AI saying “let me rebuild Postgres for you” or “give me an hour, I’ll recreate Django from scratch.” Even if it could, why would it? The agent’s goal is to solve your problem well and quickly. uv pip install django is faster and more reliable than conjuring up a bespoke web framework.

At the macro level, frameworks, databases, runtimes, open source is safe.

Will AI coding costs make open source irrelevant?

Paul raised an important point: what happens when AI pricing subsidies end and costs go up 5x? My take is that hardware costs are dropping even faster.

NVIDIA’s latest inference hardware is roughly 10x cheaper per token than two years ago:

“NVIDIA GB200 NVL72 with extreme hardware and software codesign delivers more than 10x more tokens per watt, resulting in one-tenth the cost per token.”

And the Apple Silicon trajectory means serious local model capability is coming to everyone’s laptop. The bubble isn’t as extreme as people imagine.

How should developers work with AI agents on open source projects?

We also dug into the “just send it” overnight agent workflow – and neither of us is a fan. Working in small, reviewable chunks is the way. Think spec-driven development, not “agents devour this, I’ll see you in the morning.”

Our job was never to type characters. It’s to ship quality software. If you apply engineering discipline – specs, tests, architecture decisions – then AI-assisted code is absolutely something you can put your name on. Paul shared his crisis of confidence the first time he hit Enter on twine upload for an AI-assisted package. I think a lot of developers can relate to that moment. But the question comes down to: did you ship something well-built that serves a purpose? If yes, the tooling you used to get there matters a lot less than you think.

Here’s the workflow that actually works:

1. Choose your frameworks and specify your stack up front
2. Work in small, reviewable chunks – not overnight agent runs
3. Use spec-driven development with tests and architecture decisions
4. Review all AI-generated output before shipping

Will AI kill open source? The verdict

Micro open source is probably toast. The big building blocks aren’t going anywhere. But the key is to stay in the architect’s seat – choose your frameworks, specify your stack, review the output. Be the architect handing specs to the contractor, and don’t give that role away to the AI.

There’s a lot more in the full conversation including anti-AI vigilante groups shaming people for publishing agent-assisted packages, the open source gift economy, and why none of us really know where this is all heading yet.

Watch the full video with Paul Everitt →