I think this space is very untapped. Models are interesting, but I am absolutely obsessed with some things I've been researching/working on for the past few years:
Fractal tool discovery: tool taxonomy where an agent can "drill deeper" to find what specific tool it's looking for. Helps if/when polluting context with a zillion (mostly unnecessary) tools.
Leveraging splay trees: this is my favorite data structure and I think relatively unused in the context of agents/harnesses. A lot of times, recently-used workflows/tool-chains will be used again, so having those at the top of the search hierarchy is an awesome optimization.
Virtual containerized notebooks: models working in sandboxed (WASI) Python notebooks is incredible. Even local models (if given enough time) will usually converge on a good solution. Being able to mount tools/resources/fs is again, imo quite untapped. Some problems here are running native things (thing numpy/pandas) in containers is a nightmare (or impossible).
Anyway, happy to see other folks seriously doing stuff in this space. If anyone wants to collaborate on anything don't hesitate to reach out :) I'm also actively looking for a job or some contract gigs.
> Helps if/when polluting context with a zillion (mostly unnecessary) tools.
Ugh, I can't help but respond to this one point. The fact that this is even an issue in the current year just tells us how screwed the software field is in a lot of ways. I don't mean that in existential terms, but of how divided we've become in terms of what's happened to human reasoning. On the one hand, you have people who apply deep thinking to develop the sort of approaches you described, and there's the exponentially growing segment of not-even-programmers who seem to never ask themselves whether any of their ideas have any sort of consequences.
Take MCPs for instance. Sure, I guess it can sometimes make sense to have a stateful API that is optimized for agents. Yet, more often than not, these MCPs frontload a ton of context where it's not needed, and solve problems where none existed. Merely sticking an API (MCP) in front of an API (CLI, REST, GraphQL) without a benefit that can be explained in a single sentence is lunacy and demonstrates a real lack of complex thinking.
> tells us how screwed the software field is in a lot of ways
But, you just very clearly described why human software engineers are in higher demand than ever before.
It's always been "screwed" in the sense that everyone else sucks at wielding the power.
What we're actually witnessing is a watershed moment where a lot of technologically illiterate people are getting left behind. Those of us on the literate side are left to fight amongst ourselves and the powers that be for control over the future. We have actually been doing an alright job all things considered (else this conversation wouldn't be happening). Politicians are aging out and we're the adults in the room now.
Your point is valid for individual developers or small teams of technical folks.
However, MCPs are a godsend to anyone trying to manage access to systems (especially SaaS systems) in a large organization that includes a ton of non-technical employees. We use at least a couple of SaaS products where RBAC is severely limited in what it can govern or where API keys do not have any kind of governance so handing one out is basically handing out read/write on the entire system. Being able to configure an MCP to only have read endpoints enabled it a very useful piece of duct tape. Obviously it would be great if the underlying API was just designed with proper governance but that's out of the user's control.
all other tools, mcps, apis, whatever are encapsulated by the one interface. new tools don't bloat the agent's context, and it can write its own code to perform more advanced and batch operations against the available tools (executed within a sandbox).
Fractal tool discovery is a fascinating idea! Have you worked on implimenting this into any agent harnesses already to any success? My first impression is allowing the agent to fork itself, not unlike launching a subagent, then returning it's response back to the main agent.
I've also explored s/Fractal tool discovery/Skill tree approaches, seems to work pretty well when you stick the equivalent of XREFs in the frontmatter.
It's kind of self-expanding/looping; fractal is just a cute name I like, but it's technically a directed cyclic graph (since you always have/want cycles).
I've always referred to it in UX terms. "Progressive Disclosure" -- It pulls more context as needed.
In this way, I tend to think of the context environment on an agent is the "agent nav" -- it presents context, allows progressive disclosure, and if poorly designed, makes the agent flounder as a poorly designed UI/UX does.
i have found agents to be excellent at using CLIs, which are fractal-like. i built this reddit ads api cli and my agent immediately starting introspecting it in a "drill deeper" manner:
For some reason they're comparing their harness running on Astra xhigh to Codex with Astra max?
---
Also worth noting that OpenAI just added support for async tool calling to their harness, which isn't 1:1 with this approach, but is slowly ramping up in being able to provide something similar.
It results in token savings similar in scale to those displayed here by Unreal.
---
My harness has used a slightly fancier version of the approach that Unreal is using since ~Feb, and... it definitely works excellently, but it's also assuredly smoother with Astra and other recent models that are more aware of async tool calling.
Seems to do the job and reduce usage; I just ran Astra for ~5 hours (using a goal) and it used the last 30% of my usage. And now they released GPT-6 Sol and Luna (which is basically 5.6 Sol and Luna, but a bit better and also 50% cheaper) ;_;
Perfect, I will incorporate this as default as well as the commit from the other guy into my own codex fork https://github.com/AmazingTurtle/codex btw. I'm rebasing on 0.156.0 right now
the headline chart was lazy on our part, thanks for the flag and I’ll update it.
each individual benchmark that is combined into agentic coding index was compared on xhigh between ua / codex / pi, and headline improvement was calculated on xhigh, but then agentic coding index pareto chart by default includex codex max, hense the confusion.
I had to scroll to the bottom to realize that this had absolutely nothing to do with Unreal Engine or Epic. There's no reason to think that Epic wouldn't have an "Unreal Labs" creating harnesses to help them with software engineering.
This plainly seems like a trademark issue in progress considering it's in the same exact domain and considering how many others were confused the way I probably was.
I liked the 3D effects on the homepage of unreal labs https://unreallabs.ai/ - so I was sure it's related to 3D engine makers, and of course great idea to have an agent for that too.
Unreal is trademarked. This is not in question. And personally I 100% thought this was an agent specifically for dealing with the Unreal Engine, and was interpreting all of that information in that context. Very weird name for a company/product.
Same. Specially given how some harnesses connect with blender for 3D asset generation and creating real 3D worlds like those architecture examples but many of the 1-shot game examples too.
>Computer software, namely, game engine software for video game development and operation; Computer software, namely, software development tools for the creation of computer-generated imagery and graphics for the production of video games; Computer software, namely, software development tools for the creation of computer-generated imagery and graphics for the production of content for virtual worlds and 3D platforms; Computer software, namely, software development tools for the creation of computer-generated imagery and graphics for the production of motion pictures, television shows, videos, 3D animations, 3D simulations, 3D visualizations, virtual reality motion pictures, virtual reality television shows; Computer software, namely, software development tools for the creation of computer-generated imagery and graphics for the production of virtual reality video games; Virtual reality game software; Virtual reality software for creating multimedia content; Augmented reality game software; Augmented reality software for use in mobile devices for integrating electronic data with real world environments for the purposes of entertainment
Maybe if UnrealLabs were a banana company. But they are a software company. The trademark they have on "Unreal" is for computer software and lists a bunch of domains related to the software development of video games/3d models.
Right! It’s not really rocket science. I’ve been thinking of building a general “wait” command that just blocks until some event occurs. Still more “active” waiting, but as far as I understand the efficiency problem comes from polling.
OK, but isn't this just programmatic tool calling, which is beneficial in some cases but not others?
This is available in DSH via PTC mode.
Basically, instead of the completion API returning a sequence of tool calls, it returns a program that invokes the tools.
I think most are returning Typescript program
With ordinary tool calling, you may have to wait for a tool to return its result to the completion API before the model can continue and issue the next tool call.
With PTC, the generated program can invoke independent tools asynchronously, collect their results, and dispatch the relevant results back to the completion API in a single turn.
Claude and Codex added this as well, but there are some papers on this that show it's not good across all tasks, and models need to be trained for this specific technique, which is what AI labs are doing now.
not a fan of README's where there's no clear way to run the program, like a getting started or how to build. i think this is the opposite of a docu-monster where AI documents everything, but clearly more documentation is better than lack of.
I'm unlocking savings (in both speed and costs) but using a different way of thinking by integrating Decision models:
In short, LLMs spend so many turns on discovery, triage, and safety checks for everyday tasks. A decision model cuts those turns by preparing the relevant context before the prompt and handling the simple judgments itself, so the LLM only does the real work.
I don't mean to be super negative, but I read through this all the way and still have no idea what it does or how it works. I think the problem statement needs to be outlined much better, and concrete examples of turns with/without your solution would be helpful to illustrate the difference.
gpt 6 sol already made a lot of progress with caches
i have a feeling unreal agent might have decided to release now rather than getting sherlocked
i just think its very risky right now to spend too much time building harnesses or anything on top of codex or claude simply because frontier labs will just absorb whatever works
I assume the bigger problem is the cache expiring while you wait for the tool call to complete. A bunch of hosted providers only keep the cache alive for ~5 minutes - if you sit there waiting for a 5 minute tool call, you get to pay to reload the entire context into cache
If the frontier labs (well, I guess just Anthropic) would go full OAuth support even on a subscription we would see, an even bigger, explosion in harness improvements. I maintain that there is a ton of low-hanging fruit and new ideas/concepts that should be tried but the costs are holding people back (using API pricing only).
It's both expensive to test alternative harnesses and it's expensive to develop them (if using API pricing).
Crush [1] has had async tools for a long time, as has Claude Code.
They work well, except that the agent will often simply call wait() immediately after, and also do it with a longer timeout, causing crashes/hangs to really slow down the process; whereas immediate tool calls have a built-in 30s timeout in most harnesses.
I imagine this one will suffer from similar problems.
For everyone self-hosting models, optimising for cost is an anti-feature. It makes the results worse for no benefit (except a little speed).
What I'd love to see is a harness that deeply optimises for the best results obtainable out of non-frontier models. Many of these have 1M context windows, and most of it remains unused and under utilised in these harnesses, in my opinion.
Speed is actually the thing stopping me using local models more. Qwen 3.8 27b is surprisingly capable, but spends a lot of time/tokens brute forcing problems until it gets it right. The longer task times mean that to fully utilize my attention I need more tasks running concurrently, and the increased context switching just feels bad and leads to me making worse decisions.
I think this is what I’ve been waiting for! Live interactions with models are fundamentally asynchronous! This will be great for interactivity.
What a good time for harness design. Just while OpenCode is growing up a bit and focusing on their harness. I’m delighted to see people focusing on good general solid harness design principles.
Someone make an OpenCode API (the best general agent end UI API I know) compatible server for it!
Sucks that I haven’t upgraded my client to V2 yet and I don’t really want to build on an outdated API ...
Edit: It would be cool to get streaming responses :) But I understand (and actually applaud) that the authors seem to have been very focused on the core mechanics.
The basic asynchronous approach is potentially interesting, but:
1. Agents usually depend on output of the commands they're running in order to make decisions about what to do next, so how do they behave while they're "waiting around" for the output they need?
2. Agents can _already_ run software async, via multiple mechanisms: raw CLI tools like "nohup", literally running tools in parallel (I see Sol do this often in the Opencode TUI harness), and using parallel sub-agents to e.g. research in parallel.
Thus I wonder, how much does this really improve speed vs only improving the "appearance" of getting more done faster?
So I commented on this in passing in a deeper thread (in re: the potential trademark issue - https://news.ycombinator.com/item?id=49807884), but I think this is serious enough for its own top-level thread.
How likely is it that they get sued into the ground in a year? They might have a strong suite of offerings even as soon as six months from now, but if the essence of a company's brand seems at jeopardy from the start, can I take the risk as a potential customer that they'd survive that kind of action?
When did we just give up on the challenge of giving something a unique name? I see products launch everyday on HN now that just take a well known brand and use it as their own. I thought this would be a feature for Unreal engine.
In my projects i use unreal and me and my team have tested out multiple things. We have settled on no MCP complications whatsoever other than simply exposing the python scripting from the editor + a export step that can write the blueprints and asset data into plaintext so that the bot can grep them. This has given by far the strongest results, and we now have no issues having the agents edit game code and do operations.
We found this massively outperforms any kind of agent like this and the official unreal MCP systems. Its similar to the Blender MCP which also just exposes scripting + very minimal api to claude code/others.
Honestly the fact that we're still modeling agent harnesses like chat and strapping them into shell sessions instead of building async actor systems with sandboxed OS functionality access actors is bananas to me. So much easy wins can be had just by building on right abstractions... Hopefully will get enough time to play with this idea soon on my own.
I'm actually taking the time to hash out the details to do this as a side experiment project, but basically what I read this project as showing is that if you leverage asynchrony you can let agents be more efficient - and you get this "for free" if you model your "agents" as actors that interact with the system through message passing. Then all the system operations become messages to different actors, need to read a file => message the fs actor => get reply from FS actor as a message when it's done. You'd probably need some out of mailbox ways to share blob resources and sockets for realtime (audio), but for the most part simple message passing should handle most of what LLM agents do.
Actors can have identities and roles for RBAC, etc. you - cross agent communication is the same as sending any other message to a actors.
Not to mention that actors can be on your device, another device, etc. whatever the router can resolve - it's transparent to the agents.
I’d think the approach in this repo gets close to that, no? I also think LLM harnesses should just support typical async i/o.
Let a model get notifications when something it has accessed/ subscribed to changes, a tool produces output or it gets some other kind of message (for example by another AI or flesh agent). Wait until the next turn or wake it up. It can still decide to do nothing and wait.
That's the reason I'm excited about the orchestrator because it would let me build my ideal UX on top.
If you move away from the idea that agents are chat streams and treat them as processes/actors then you can start letting agents represent themselves/build their own interfaces.
And if you build enough introspection into the protocol because everything is message based you can have other agents build interfaces for them.
So like either standard GUI, or a voice assistant talking to you and delegating to agents, etc.
It's not an idea it's a way of thinking about agent systems - basically an agent OS.
I think you nailed it. I have my own agent harness that's designed with inspiration from erlang/elixir. It seems to be a natural fit for indeterministic output and failures.
https://tau-agent.dev/ in my setup. Many sessions each multiagent, in different sandboxes, sending messages. Also nothing in the main article that Tau wouldn't have. I just don't have stamina for more "marketing". Async tools ... sooo basic. :D
Async, as I understand it, means that the agent never has to actively wait for things to complete, so it can do work while it waits and can receives the result at some later point which it can then react to. I don’t know whether Eve has that feature, it would be cool. Should not meaningfully influence its ability to be deployed serverlessly.
I was imagining a talking head avatar rendered in Unreal Engine, with lip sync and facial expressions driven by a multimodal LLM that produces the speech.
Codex offers an OAuth endpoint, which can be used in any harness of your choice. Note this is separate from their app-server setup; in this setup, you talk directly to the API.
I think this space is very untapped. Models are interesting, but I am absolutely obsessed with some things I've been researching/working on for the past few years:
Fractal tool discovery: tool taxonomy where an agent can "drill deeper" to find what specific tool it's looking for. Helps if/when polluting context with a zillion (mostly unnecessary) tools.
Leveraging splay trees: this is my favorite data structure and I think relatively unused in the context of agents/harnesses. A lot of times, recently-used workflows/tool-chains will be used again, so having those at the top of the search hierarchy is an awesome optimization.
Virtual containerized notebooks: models working in sandboxed (WASI) Python notebooks is incredible. Even local models (if given enough time) will usually converge on a good solution. Being able to mount tools/resources/fs is again, imo quite untapped. Some problems here are running native things (thing numpy/pandas) in containers is a nightmare (or impossible).
Anyway, happy to see other folks seriously doing stuff in this space. If anyone wants to collaborate on anything don't hesitate to reach out :) I'm also actively looking for a job or some contract gigs.
Fun times ahead.
> Helps if/when polluting context with a zillion (mostly unnecessary) tools.
Ugh, I can't help but respond to this one point. The fact that this is even an issue in the current year just tells us how screwed the software field is in a lot of ways. I don't mean that in existential terms, but of how divided we've become in terms of what's happened to human reasoning. On the one hand, you have people who apply deep thinking to develop the sort of approaches you described, and there's the exponentially growing segment of not-even-programmers who seem to never ask themselves whether any of their ideas have any sort of consequences.
Take MCPs for instance. Sure, I guess it can sometimes make sense to have a stateful API that is optimized for agents. Yet, more often than not, these MCPs frontload a ton of context where it's not needed, and solve problems where none existed. Merely sticking an API (MCP) in front of an API (CLI, REST, GraphQL) without a benefit that can be explained in a single sentence is lunacy and demonstrates a real lack of complex thinking.
> tells us how screwed the software field is in a lot of ways
But, you just very clearly described why human software engineers are in higher demand than ever before.
It's always been "screwed" in the sense that everyone else sucks at wielding the power.
What we're actually witnessing is a watershed moment where a lot of technologically illiterate people are getting left behind. Those of us on the literate side are left to fight amongst ourselves and the powers that be for control over the future. We have actually been doing an alright job all things considered (else this conversation wouldn't be happening). Politicians are aging out and we're the adults in the room now.
Your point is valid for individual developers or small teams of technical folks.
However, MCPs are a godsend to anyone trying to manage access to systems (especially SaaS systems) in a large organization that includes a ton of non-technical employees. We use at least a couple of SaaS products where RBAC is severely limited in what it can govern or where API keys do not have any kind of governance so handing one out is basically handing out read/write on the entire system. Being able to configure an MCP to only have read endpoints enabled it a very useful piece of duct tape. Obviously it would be great if the underlying API was just designed with proper governance but that's out of the user's control.
MCP’s make sense because, people want to click the + icon on chatgpt or claude to connect their data. nobody cares if you can do it manually
Isn't that what codemode is for?
mcp with 2 tools: execute(code) and search(query)
all other tools, mcps, apis, whatever are encapsulated by the one interface. new tools don't bloat the agent's context, and it can write its own code to perform more advanced and batch operations against the available tools (executed within a sandbox).
executor is a great implementation of this - https://executor.sh
opencode v2 also provides its own native implementation
Have you had workflows/benchmarks to test this on? I'm primarily interested if there are real use cases that would benefit.
Fractal tool discovery is a fascinating idea! Have you worked on implimenting this into any agent harnesses already to any success? My first impression is allowing the agent to fork itself, not unlike launching a subagent, then returning it's response back to the main agent.
I've also explored s/Fractal tool discovery/Skill tree approaches, seems to work pretty well when you stick the equivalent of XREFs in the frontmatter.
What makes it fractal?
It's kind of self-expanding/looping; fractal is just a cute name I like, but it's technically a directed cyclic graph (since you always have/want cycles).
I think recursive fits better, but I get it.
I've always referred to it in UX terms. "Progressive Disclosure" -- It pulls more context as needed.
In this way, I tend to think of the context environment on an agent is the "agent nav" -- it presents context, allows progressive disclosure, and if poorly designed, makes the agent flounder as a poorly designed UI/UX does.
It’s more so the divide and conquer. Recursion vs iterative is tangential.
But not fractal, because that implies self-similarity independent of scale
i have found agents to be excellent at using CLIs, which are fractal-like. i built this reddit ads api cli and my agent immediately starting introspecting it in a "drill deeper" manner:
https://github.com/genei-Ltd/reddit-ads-cli
bonus if it spits out Double Kill, Mega Kill, GodLike! when saving tokens
The headline graph is kind of bizarre.
For some reason they're comparing their harness running on Astra xhigh to Codex with Astra max?
---
Also worth noting that OpenAI just added support for async tool calling to their harness, which isn't 1:1 with this approach, but is slowly ramping up in being able to provide something similar.
A big part of why Codex uses so many tokens is that it basically hot loops on polling tasks it starts for... absolutely no good reason: https://www.reddit.com/r/codex/comments/1wdlp7q/weve_discove...
I fixed it on my fork of Codex too, also back in Jan/Feb – I keep this patch rebased, for anyone who wants it: https://github.com/tekacs/codex/commit/9ffcf8db9078eae43d411...
It results in token savings similar in scale to those displayed here by Unreal.
---
My harness has used a slightly fancier version of the approach that Unreal is using since ~Feb, and... it definitely works excellently, but it's also assuredly smoother with Astra and other recent models that are more aware of async tool calling.
To reduce the codex polling I slapped this into my ~/.codex/config.toml:
Seems to do the job and reduce usage; I just ran Astra for ~5 hours (using a goal) and it used the last 30% of my usage. And now they released GPT-6 Sol and Luna (which is basically 5.6 Sol and Luna, but a bit better and also 50% cheaper) ;_;Perfect, I will incorporate this as default as well as the commit from the other guy into my own codex fork https://github.com/AmazingTurtle/codex btw. I'm rebasing on 0.156.0 right now
the headline chart was lazy on our part, thanks for the flag and I’ll update it.
each individual benchmark that is combined into agentic coding index was compared on xhigh between ua / codex / pi, and headline improvement was calculated on xhigh, but then agentic coding index pareto chart by default includex codex max, hense the confusion.
Sounds like a trademark issue when Epic ships a wildly popular Unreal Engine
I had to scroll to the bottom to realize that this had absolutely nothing to do with Unreal Engine or Epic. There's no reason to think that Epic wouldn't have an "Unreal Labs" creating harnesses to help them with software engineering.
This plainly seems like a trademark issue in progress considering it's in the same exact domain and considering how many others were confused the way I probably was.
I liked the 3D effects on the homepage of unreal labs https://unreallabs.ai/ - so I was sure it's related to 3D engine makers, and of course great idea to have an agent for that too.
yep, can confirm i was confused too
lol it took me to read this comment to find out this has nothing to do with Unreal.
People, I'm begging you, please talk to a lawyer before launching.
Yeah there’s no way they can use this name for software, it is a complete ripoff of their brand
But I don't think you can trademark a generic word like Unreal...
Unreal is trademarked. This is not in question. And personally I 100% thought this was an agent specifically for dealing with the Unreal Engine, and was interpreting all of that information in that context. Very weird name for a company/product.
Same, I thought this was going to be a plugin for creating content in Unreal engine.
Same. Specially given how some harnesses connect with blender for 3D asset generation and creating real 3D worlds like those architecture examples but many of the 1-shot game examples too.
Likewise. This seems to be the norm for AI people: act as if.
They've trademarked it for quite a few closely-related domains: https://tsdr.uspto.gov/#caseNumber=87709072&caseSearchType=U...
>Computer software, namely, game engine software for video game development and operation; Computer software, namely, software development tools for the creation of computer-generated imagery and graphics for the production of video games; Computer software, namely, software development tools for the creation of computer-generated imagery and graphics for the production of content for virtual worlds and 3D platforms; Computer software, namely, software development tools for the creation of computer-generated imagery and graphics for the production of motion pictures, television shows, videos, 3D animations, 3D simulations, 3D visualizations, virtual reality motion pictures, virtual reality television shows; Computer software, namely, software development tools for the creation of computer-generated imagery and graphics for the production of virtual reality video games; Virtual reality game software; Virtual reality software for creating multimedia content; Augmented reality game software; Augmented reality software for use in mobile devices for integrating electronic data with real world environments for the purposes of entertainment
Those do all seem directly tied to what Unreal Engine does i.e. graphics
>"But I don't think you can trademark a generic word like Unreal..."
Sure you can, trademarks are contextual. If they were a landscaping business it wouldn't matter. But within the same industry absolutely.
I guess? but we are talking Game engine vs AI harness...
The USPTO really isn't interested in those sorts of semantics. This would be a slam dunk for Epic.
Apple? https://en.wikipedia.org/wiki/Apple_Corps_v_Apple_Computer
https://en.wikipedia.org/wiki/Sosumi
iPhone? https://en.wikipedia.org/wiki/IPhone_(trademark)
An XR glasses company, Nreal, changed their name to XREAL, allegedly because of Epic Games.
It doesn't matter if they can or can't, it only matters if you can afford the lawsuit.
Suggestion: rename it to "Unreliable Agent"
i mean unreal is such a generic word, i doubt it epic would win the lawsuit
Maybe if UnrealLabs were a banana company. But they are a software company. The trademark they have on "Unreal" is for computer software and lists a bunch of domains related to the software development of video games/3d models.
Funny, I solved this problem by having Claude write a pi extension
https://github.com/Pyrolistical/pi-notify
Now my pi agent setups its own trigger to notify itself when a background process is done
Right! It’s not really rocket science. I’ve been thinking of building a general “wait” command that just blocks until some event occurs. Still more “active” waiting, but as far as I understand the efficiency problem comes from polling.
Oh very nice, can you also compare it to https://maki.sh?
Would be interesting to compare to a harness optimizing for cost reduction too.
The techniques appear to be orthogonal, so you might be able to combine them
OK, but isn't this just programmatic tool calling, which is beneficial in some cases but not others?
This is available in DSH via PTC mode.
Basically, instead of the completion API returning a sequence of tool calls, it returns a program that invokes the tools.
I think most are returning Typescript program
With ordinary tool calling, you may have to wait for a tool to return its result to the completion API before the model can continue and issue the next tool call.
With PTC, the generated program can invoke independent tools asynchronously, collect their results, and dispatch the relevant results back to the completion API in a single turn.
Claude and Codex added this as well, but there are some papers on this that show it's not good across all tasks, and models need to be trained for this specific technique, which is what AI labs are doing now.
its not really, model expresses tool calls as a list of bash tools
DSH also does this, but Bash calls are part of the TypeScript program as response in PTC mode.
not a fan of README's where there's no clear way to run the program, like a getting started or how to build. i think this is the opposite of a docu-monster where AI documents everything, but clearly more documentation is better than lack of.
I came to say the same thing. Zero info about how I would use this once it’s cloned.
It’s a library and there are usage examples.
> but clearly more documentation is better than lack of
I tend to disagree. This makes it very high signal. So far my agent has been successful at navigating the repo.
This is so interesting.
I'm unlocking savings (in both speed and costs) but using a different way of thinking by integrating Decision models:
In short, LLMs spend so many turns on discovery, triage, and safety checks for everyday tasks. A decision model cuts those turns by preparing the relevant context before the prompt and handling the simple judgments itself, so the LLM only does the real work.
Here is the full article I published yesterday with in-house benchmark: https://tawfeks.github.io/decisionkit/
What do you think, guys? I need feedback on this.
I don't mean to be super negative, but I read through this all the way and still have no idea what it does or how it works. I think the problem statement needs to be outlined much better, and concrete examples of turns with/without your solution would be helpful to illustrate the difference.
happy to see more harnesses in the Go space, i've had a fun time building https://github.com/ChaseRensberger/wingman
i think the language primitives map well to the natural desire to use these tools across networks and in concurrent workloads.
another Go harness i keep up to date with is: https://github.com/boldsoftware/shelley (exe.dev team)
I don't really understand how async tool calls translate to token savings
It says that it removes tokens wasted while a model is waiting on synchronous tool calls. What tokens exactly is Pi using when waiting?
I think a running agent periodically checks if a process it started has finished.
that shouldn't be what eats up tokens tho
gpt 6 sol already made a lot of progress with caches
i have a feeling unreal agent might have decided to release now rather than getting sherlocked
i just think its very risky right now to spend too much time building harnesses or anything on top of codex or claude simply because frontier labs will just absorb whatever works
I assume the bigger problem is the cache expiring while you wait for the tool call to complete. A bunch of hosted providers only keep the cache alive for ~5 minutes - if you sit there waiting for a 5 minute tool call, you get to pay to reload the entire context into cache
"More tool work per model turn" could reduce the number of cache reads (or even cache misses) and associated cost?
I suppose GPT models throw shade when your input is less favorable to Codex:
“On the surface, Unreal Agent achieves the same outcomes with fewer model turns and fewer input tokens.”
(Emphasis mine)
If the frontier labs (well, I guess just Anthropic) would go full OAuth support even on a subscription we would see, an even bigger, explosion in harness improvements. I maintain that there is a ton of low-hanging fruit and new ideas/concepts that should be tried but the costs are holding people back (using API pricing only).
It's both expensive to test alternative harnesses and it's expensive to develop them (if using API pricing).
Crush [1] has had async tools for a long time, as has Claude Code.
They work well, except that the agent will often simply call wait() immediately after, and also do it with a longer timeout, causing crashes/hangs to really slow down the process; whereas immediate tool calls have a built-in 30s timeout in most harnesses.
I imagine this one will suffer from similar problems.
This problem has a few rather trivial solutions.
Like what?
Don't give the agent any kind of wait or sleep tools and deny any attempt to read status without new progress notifications.
For everyone self-hosting models, optimising for cost is an anti-feature. It makes the results worse for no benefit (except a little speed).
What I'd love to see is a harness that deeply optimises for the best results obtainable out of non-frontier models. Many of these have 1M context windows, and most of it remains unused and under utilised in these harnesses, in my opinion.
Speed is actually the thing stopping me using local models more. Qwen 3.8 27b is surprisingly capable, but spends a lot of time/tokens brute forcing problems until it gets it right. The longer task times mean that to fully utilize my attention I need more tasks running concurrently, and the increased context switching just feels bad and leads to me making worse decisions.
Totally agree, Qwen 3.8 is a pain to wait for. Glimmer much less so!
This is exactly what Swival was built for: https://swival.dev
Is it? The tagline says “Designed for tight context windows and limited resources.”
it doesnt make the result worse
I think this is what I’ve been waiting for! Live interactions with models are fundamentally asynchronous! This will be great for interactivity.
What a good time for harness design. Just while OpenCode is growing up a bit and focusing on their harness. I’m delighted to see people focusing on good general solid harness design principles.
Someone make an OpenCode API (the best general agent end UI API I know) compatible server for it!
Sucks that I haven’t upgraded my client to V2 yet and I don’t really want to build on an outdated API ...
Edit: It would be cool to get streaming responses :) But I understand (and actually applaud) that the authors seem to have been very focused on the core mechanics.
Also note that this works with the responses API!
The basic asynchronous approach is potentially interesting, but:
1. Agents usually depend on output of the commands they're running in order to make decisions about what to do next, so how do they behave while they're "waiting around" for the output they need?
2. Agents can _already_ run software async, via multiple mechanisms: raw CLI tools like "nohup", literally running tools in parallel (I see Sol do this often in the Opencode TUI harness), and using parallel sub-agents to e.g. research in parallel.
Thus I wonder, how much does this really improve speed vs only improving the "appearance" of getting more done faster?
I don't think the point is speed per se but rather reducing overhead of checking on their completion.
So I commented on this in passing in a deeper thread (in re: the potential trademark issue - https://news.ycombinator.com/item?id=49807884), but I think this is serious enough for its own top-level thread.
How likely is it that they get sued into the ground in a year? They might have a strong suite of offerings even as soon as six months from now, but if the essence of a company's brand seems at jeopardy from the start, can I take the risk as a potential customer that they'd survive that kind of action?
I think highly unlikely, they'll get slapped with a cease & desist and just change the name.
I am super confused. Is this nothing to do with Unreal? I was assuming an MCP agent for creating rendered worlds for games etc.
Is there a TUI or GUI I can use this with? Or is unreal-agent-runner intended to be called from an existing app? How does that work?
When did we just give up on the challenge of giving something a unique name? I see products launch everyday on HN now that just take a well known brand and use it as their own. I thought this would be a feature for Unreal engine.
I was really hoping that Epic had released a harness that plays games.
In my projects i use unreal and me and my team have tested out multiple things. We have settled on no MCP complications whatsoever other than simply exposing the python scripting from the editor + a export step that can write the blueprints and asset data into plaintext so that the bot can grep them. This has given by far the strongest results, and we now have no issues having the agents edit game code and do operations.
We found this massively outperforms any kind of agent like this and the official unreal MCP systems. Its similar to the Blender MCP which also just exposes scripting + very minimal api to claude code/others.
OP is a coding agent and harness tool named Unreal, not the game engine by Epic.
Completely misread the github and understood this as a agent inside unreal as a plugin...
The critique of CLI-oriented SDKs is fair. I'd want to see how it behaves on long-horizon tasks where the "no sub-agents" constraint starts to bite
This feels like it's begging for a lawsuit from Epic.
Epic harness -- unreal agent!
Honestly the fact that we're still modeling agent harnesses like chat and strapping them into shell sessions instead of building async actor systems with sandboxed OS functionality access actors is bananas to me. So much easy wins can be had just by building on right abstractions... Hopefully will get enough time to play with this idea soon on my own.
Do you have any examples of this approach?
I'm actually taking the time to hash out the details to do this as a side experiment project, but basically what I read this project as showing is that if you leverage asynchrony you can let agents be more efficient - and you get this "for free" if you model your "agents" as actors that interact with the system through message passing. Then all the system operations become messages to different actors, need to read a file => message the fs actor => get reply from FS actor as a message when it's done. You'd probably need some out of mailbox ways to share blob resources and sockets for realtime (audio), but for the most part simple message passing should handle most of what LLM agents do.
Actors can have identities and roles for RBAC, etc. you - cross agent communication is the same as sending any other message to a actors.
Not to mention that actors can be on your device, another device, etc. whatever the router can resolve - it's transparent to the agents.
I’d think the approach in this repo gets close to that, no? I also think LLM harnesses should just support typical async i/o.
Let a model get notifications when something it has accessed/ subscribed to changes, a tool produces output or it gets some other kind of message (for example by another AI or flesh agent). Wait until the next turn or wake it up. It can still decide to do nothing and wait.
You lambasted the interface (linear messages, chat) but then never pitched us your vision of a successor.
Your technical ideas are just implementation details behind the interface. What's your idea for a better UX?
That's the reason I'm excited about the orchestrator because it would let me build my ideal UX on top.
If you move away from the idea that agents are chat streams and treat them as processes/actors then you can start letting agents represent themselves/build their own interfaces.
And if you build enough introspection into the protocol because everything is message based you can have other agents build interfaces for them.
So like either standard GUI, or a voice assistant talking to you and delegating to agents, etc.
It's not an idea it's a way of thinking about agent systems - basically an agent OS.
I think you nailed it. I have my own agent harness that's designed with inspiration from erlang/elixir. It seems to be a natural fit for indeterministic output and failures.
https://tau-agent.dev/ in my setup. Many sessions each multiagent, in different sandboxes, sending messages. Also nothing in the main article that Tau wouldn't have. I just don't have stamina for more "marketing". Async tools ... sooo basic. :D
Why don't you explain what's different?
Seeing this isnt Unreal Engine related, I do wonder if anyone is building tools for unreal engine.
Does it support subscriptions ? If not it's a no go for me.
This is a fairly low level library focusing on the core harness mechanics.
But the selling point is "its cheaper to run on api compared to codex/cc" ... but I, and a lot of folks, don't use API.
how does this compare to eve? does async mean serverless?
Async, as I understand it, means that the agent never has to actively wait for things to complete, so it can do work while it waits and can receives the result at some later point which it can then react to. I don’t know whether Eve has that feature, it would be cool. Should not meaningfully influence its ability to be deployed serverlessly.
aka., we want your usage data please use us blah blah blah
why use that name, cease and desist on its way folks
what are you the name police, I'm naming my next project greg
that's like the worst name you could have picked
Your Gimp Agent is ready.
omp.sh does this way better by just allowing structural toolcall execution in eval with python/js.
unfortunate name. misleads
Not to be confused with Epic's Unreal Engine. Epic will probably be having words with this startup about the name.
VERY MUCH to be confused, which is exactly why Epic will doubtless be having many w… no, I'm sorry, I think they'll have one word. 'Nope'
Happy with swival.dev ...
This is not an Unreal Engine agents? Very disappointed
Mildly disappointed that this has nothing to do with Unreal Engine, the popular game engine.
Yeah, I was hoping it would be something to make it easier for agents to interface with Unreal Engine games.
I was imagining a talking head avatar rendered in Unreal Engine, with lip sync and facial expressions driven by a multimodal LLM that produces the speech.
Unreal 5.8 has a MCP plugin
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Can I use it with my codex sub?
I think the biggest selling point of a codex sub vs a claude sub is that you can use codex subs in any harness you want.
I didn't know, thank you!
Codex offers an OAuth endpoint, which can be used in any harness of your choice. Note this is separate from their app-server setup; in this setup, you talk directly to the API.
I’m sad to discover that this is not a mod for Unreal to extend TacOps with a clandestine gameplay mode which prioritizes stealth and spy craft.