Effective harnesses for long-running agents
anthropic.com70 points by diwank 7 hours ago
70 points by diwank 7 hours ago
One of the things that makes it very difficult to have reasonable conversations about what you can do with LLMs is the effort-to-outcome curve is basically exponential - with almost no effort, you can get 70% of the way there. This looks amazing, and so people (mostly executives) look at this and think, “this changes everything!”
The problem is the remaining 30% - the next 10-20% starts to require things like multi-agent judge setups, external memory, context management, and that gets you to something that’s probably working but you sure shouldn’t ship to production. As to the last 10% - I’ve seen agentic workflows with hundreds of different agents, multiple models, and fantastically complex evaluation frameworks to try to reduce the error rates past the ~10% mark. By a certain point, the amount of infrastructure and LLM calls are running into several hundred dollars per run, and you’re still not getting guaranteed reliable output.
If you know what you’re doing and you know where to fit the LLMs (they’re genuinely the best system we’ve ever devised for interpreting and categorizing unstructured human input), they can be immensely useful, but they sing a siren song of simplicity that will lure you to your doom if you believe it.
Yes, it's essentially the Pareto principle [0]. The LLM community has conflated the 80% as difficult complicated work, when it was essentially boilerplate. Allegedly LLMs have saved us from that drudgery, but I personally have found that (without the complicated setups you mention) the 80% done project that gets one shot is in reality more like 50% done because it is built on an unstable foundation, and that final 20% involves a lot of complicated reworking of the code. There's still plenty of value but I think it is less than proponents would want you to believe.
Anecdotally, I have found that even if you type out paragraph after paragraph describing everything you need the agent to take care of, it eventually feels like you could have written a lot of the code yourself with the help of a good IDE by the time you can finally send your prompt off.
Yeah, my mental model at this point is there’s two components to building a system: writing the code and understanding the system. When you’re the one writing the code, you get the understanding at the same time. When you’re not, you still need to put in that work to deeply grok the system. You can do it ahead of time while writing the prompts, you can do it while reviewing the code, you can do it while writing the test suite, or you can do it when the system is on fire during an outage, but the work to understand the system can’t be outsourced to the LLM.
Just for getting a frame of reference, how many people were involved over how much time building a workflow with hundreds of agents?
I’ve seen a couple solo efforts and a couple teams, but usually a few months. It tends to evolve as a kind of whack-a-mole situation - “we solved that failure case/hallucination, now we’re getting this one.”
Why do we need long running agents? Most of my experienced value with LLMs has been like 1 to 10 turn chats. Should they just ban longer chats to solve these issues?
…it really feels like they’re attempting to reinvent a project tracker and starting off from scratch in thinking about it.
It feels like they’re a few versions behind what I’m doing, which is… odd.
Self-hosting a plane.io instance. Added a plane MCP tool to my codex. Added workflow instructions into Agents.md which cover standards, documentation, related work, labels, branch names, adding of comments before plan, after plan, at varying steps of implementation, summary before moving ticket to done. Creating new tickers and being able to relate to current or others, etc…
It ain’t that hard. Just do inception (high to mid level details) create epics and tasks. Add personas, details, notes, acceptance criteria and more. Can add comments yourself to update. Whatever.
Slice tickets thin and then go wild. Add tickets as your working though things. Make modifications.
Why so difficult?
This was my take.
They’ve made an issue tracker out of json files and a text file.
Why not hook an mcp to an actual issue tracker?
Used an LLM to help write the following up as I’m still pretty scattered about the idea and on mobile.
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Something I’ve been going over in my head:
I used to work in a pretty strict Pivotal XP shop. PM ran the team like a conductor. We had analysts, QA, leads, seniors. Inceptions for new features were long, sometimes heated sessions with PM + Analyst + QA + Lead + a couple of seniors. Out of that you’d get:
- Thinly sliced epics and tasks - Clear ownership - Everyone aligned on data flows and boundaries - Specs, requirements, and acceptance criteria nailed at both high- and mid-level
At the end, everyone knew what was talking to what, what “done” meant, and where the edges were.
What I’m thinking about now is basically that process, but agentized and wired into the tooling:
- Any ticket is an entry point into a graph, not just a blob of text. - Epics ↔ tasks ↔ subtasks - Linked specs / decisions / notes - Files and PRs that touched the same areas
- Standards live as versioned docs, not just a random Agents.md:
- Markdown (with diagrams) that declares where it applies: tags, ticket types, modules.
- Tickets can pin those docs via labels/tags/links.
- The agent then plays something like the analyst + senior engineer role: - Pulls in the right standards automatically - Proposes acceptance criteria and subtasks - Explains why a file looks the way it does by walking past tickets / PRs / decisions
So it’s less “LLM stapled to an issue tracker” and more “that old XP inception + thin-slice discipline, encoded as a graph the agent can actually reason over.”
Did you mean plane.so instead of plane.io?
IME a dedicated testing / QA agent sounds nice but doesn't work, for same reasons as AI / human interaction. The more you try to diverge from the original dev agent's approach, the less and less chance there is that the dev agent will get to where you want it to be. Far more frequently it'll get stuck in a loop between two options that are both not what you want.
So adding a QA agent, while it sounds logical, just ends up being even more of this. Rather than converging on a solution, they just get all out of whack. Until that is solved, far better just to have your dev agent be smart about doing its own QA.
The only way I could see the QA agent idea working now is if it had the power to roll back the entire change, reset the dev agent, update the task with some hints of things not to overlook, and trigger the dev process from scratch. But that seems pretty inefficient, and IDK if it would work any better.
> Run pwd to see the directory you’re working in. You’ll only be able to edit files in this directory.
If you're using the agent to produce any kind of code that has access to manipulate the filesystem, may as well have it understand its own abilities as having the entirety of CRUD, not just updates. I could easily see the agent talking itself into working around "only be able to edit" with its other knowledge that it can just write a script to do whatever it wants. This also reinforces to devs that they basically shouldn't trust the agent when it comes to the filesystem.
As for pwd for existing projects, I start each session running tree local to the part of the project filesystem I want to have worked on.
Having done this for Gemini CLI to get it to behave well several months ago to have a non-coding LLM CLI without costs, I can attest that these tips work well across CLIs.
I wonder how good these agents would be using something like cucumber and behaviour driven development tools?
> … the model is less likely to inappropriately change or overwrite JSON files compared to Markdown files.
Very interesting.
BDSM for LLMs