A Survey of AI Agent Protocols (opens in new tab)

Is it something like this:

Ages and ages ago I was an EE turned programmer and everyone was hyping JUnit at the time. I had a customer ask for it on a project so fine I'll learn it. I kept thinking it was stupid because in my mind it barely did anything. But then I got it: it did barely do anything, but it did things you'd commonly need to do for testing and it did them in a somewhat standardized way that kept you from having to roll your own every time. Suddenly it didn't feel so stupid.

This article was written a few weeks after MCP was released and touches on why MCP is important. While I guess you could argue that technically there's nothing to it, protocols such as MCP is addressing a missing need to standardize interactions between your ai app and another service. Code needs to now be written for users, devs (apis), and ai.

https://www.willowtreeapps.com/craft/is-anthropic-model-cont...

Ok, but when posting here, can you please follow the site guidelines? They include:

"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something."

"Don't be curmudgeonly."

https://news.ycombinator.com/newsguidelines.html

HTTP was never an extraordinarily different protocol, there was really nothing to it from a technical perspective. What was important was it wrapped up a lot of the concepts from the various hypertext and linking protocols that existed into a standard that was widely supported. The standard and the compatibility across servers and clients made it important. This is generally true for protocols across the board. In fact very few standards protocols are particularly revolutionary from a technical perspective. That’s in many ways very much not the point.

It's just something that needs to be done if you want LLMs to be useful. Everything does not have to be a technological marvel.

I wonder if it's just our egos talking, telling us that for something to have value within this technical sphere it has to be complex and 'hard won'?

I see this agent stuff as a pretty basic agreement wrapped up in the vernacular of a protocol, i.e. "we'll put this stuff in the 'role' prop on our JSON", and then everyone else knows where to look for it. It's not especially important what we decide, as long as we create shared expectations.

I used to think protocols and RFCs and other products of standards bodies were the juicy 'big boy table' of tech, but then I realised: ok, it's not so complex–and perhaps doesn't itch my engineering itch–, but SOMEONE needs to take responsibility for deciding the protocol, otherwise it's just a bunch of randoms making up interfaces 1:1 with other parties and no useful tooling emerging for a long time. Best to beat it to the punch and just figure out a 'good enough' approach so we can all get on and make cool stuff.

Addendum: I will say, however, that the AI space has a particularly unhealthy attraction to naming, coining, forming myriad acronyms and otherwise confusing what should be a very simple thing by wrapping it up in a whitepaper and saying LOOK MA LOOK!

Well.. autodiff isn't particularly technically sophisticated. But I doubt that when it was first invented in the 1950s, people could have foreseen that a lot of autodiff put together can write an implementation of autodiff.

If you're interested in more technical approaches, I think they're starting to come together, slowly. I've seen several research directions (operads / cellular sheaves for a theory of multiagent collaboration, the theory of open games for compositional forwards/backwards strategy/planning in open environments) that would fit in quite nicely. And to some extent, the earliest frameworks are going to be naive implementations of these directions.

Too young, too naive.I think you just need to study the history of previous generation of protocols and standards to appreciate its importance.

HTML was XML for the web. Nothing to it from a technical perspective.

Even worse is that virtually no one in this space even recognizes the fairly rich research into “agents” throughout the history of computer science.

Mention John Holland’s work in adaptive systems, Hewitt’s actor model or even Minsky’s “Society of the Mind” and you’ll be met with blank stares.

I do believe LLMs have the potential to make these older ideas relevant again and potentially create something amazing, but sadly the ignorant hype makes it virtually impossible to have intelligent conversations about these possibilities.

> Presumably "planning" here is covered by any LLM that can do "think step by step" reasonably well?

mild disagree. 1) externalizing the plan and letting the user audit/edit the plan while its working is "tool use", yes, but a very specialcase kind of tool use that, for example, operator and deep research use Temporal for. ofc we also saw this with Devin/Manus and i kinda think they're better 2) there is a form of primitive tree search that people are doing where they can spam out several different paths and run it a few steps ahead to gain information about optimal planning. You will see this with morph's launch at AIE. 3) plan meta reflection and reuse - again a form of tool use, but the devin and allhands folks have worked on this a lot more than most.

my criticism of many agent definitions is that they generally do not take memory, planning, and auth seriously enough, and i think those 3 areas are my current bets for "alpha" in 2025.

> I haven't seen a definition that specifically encompasses both short- and long-term memory before.

here

- https://docs.mem0.ai/core-concepts/memory-types#short-term-m...

- https://x.com/swyx/status/1915128966203236571

Accurate memory access across memory types is still not really solved. That is the issue. Most agent frameworks from the main model providers are still quite spotty.

Image of a table outlining where the major frameworks are: https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_...

Here is also an article I wrote last year on the different types of memory: https://open.substack.com/pub/jdsemrau/p/memory-and-knowledg...

> It wasn't clear to me what the difference between "tool using" and "action execution" was.

doing a lot of inference here, but could be a separation between -read- tool kinda actions, and -write/execute- (like running code/sending an email, etc)

a bit weird from a coding perspective but idk

> similar to how ChatGPT's memory feature worked up until about four weeks ago

What happened four weeks ago?

I hate to say "it depends" but it, aaah, kinda depends. Nailing down a good definition of "Agent" has a been a problem dating back at least into the 1990's if not the 1980's. So, depending on which definition of "AI Agent" you're using, you arguably don't even need any LLM at all. Heck, using the most expansive definition I've seen, a mechanical thermostat counts. I don't know that I'd go that far, but I'll definitely say that I do not consider Agents to require use of LLM's.

That said, the "Agent pattern du jour" is heavily based on using LLM's to provide the "brain" of the Agent and then Tool Calling to let it do things an LLM can't normally do. But still... depending on just what you do with those tool calls and any other code that sits in your Agent implementation then it certainly could be more than "just" an LLM wrapper.

Nothing stops you from, for example, using the BDI architecture, implementing multi-level memory that's analogous to the way human memory works, wiring in some inductive learning, and throwing in some case-based reasoning, and an ontology based reasoning engine.

Most people today aren't doing this, because they're mostly johnny-come-lately's that don't know anything about AI besides what they see on Twitter, Reddit, and LinkedIn; and wouldn't know BDI from BDSM.

[1]: https://en.wikipedia.org/wiki/Belief%E2%80%93desire%E2%80%93...

Yes. Check the codebases. It's all prompting scaffolding. All of it. Chain of though, agents, tool using. It's just parsing the user inputs, adding text around it. "You are an expert X". That's the whole edifice.

I think you're right: Agents are at first just an LLM wrapper (app, can even be a Spreadsheet).

For me, the question of which protocols are used to communicate with the environment (MCP et al) ist just one and not even the most interesting question. Other questions uncover better, why agents are "a different kind of software" and why they might vastly change how we think of and use software:

- Stochastic not deterministic (evals/tests are crucial, creating reliable systems is much harder)

- conversational not forms (changes how we write software, and what we need to know for great UX)

- modalities, especially voice, change how we use computers (screens may become less important),

- batch with occasional real-time vs interactive might change how we feel that software works "for us"

- different unit economics (inference costs are significant compared to traditional run-time costs) change how software can be marketed

- data-driven capabilities on every level may change value chains and dictate how agents can work (if data is the moat, will agents need to "go to" the data owner and will be closely guarded of what they can extract/use?, much more than just traditional AAA)

- agents can be implemented in a way that they get better "themselves", because LLMs can be trained with data - will model providers capture most of the value of specialized vertical solutions? Is code less valuable than data/LLMs in the end?

- human-agent-relationship: By definition, agents act on someones behalf. This may again change how we interact with services/websites/content. Currently our personal systems are just like terminals. Will our interactions with services/websites etc. be mediated by "our" personal agents, or will we continue to use the different services, directly (and their agents, too)? Depending on that, the internet as we know it might change dramatically - services must deal more with agents than to humans, directly.

Bottom line: Agents are just an LLM wrapper, but they have the potential to dramatically change a lot of things around software. That's what's interesting about it, in my view.

i think an Agent is an LLM that interacts with the outside world via a protocol like MCP, that's a kind of REST-like protocol with a detailed description for the LLM on how to use it. An example is an MCP server that knows how to look up the price for a given stock ticker, so it enables the LLM to tell the current price for that ticker.

see: https://github.com/luigiajah/mcp-stocks

The implementation: https://github.com/luigiajah/mcp-stocks/blob/main/main.py

Each MCP endpoint comes with a detailed comment - that comment will be part of the metadata published by the MCP server / extension. The LLM reads this instruction when the MCP extension is added by the end user, so it will know how to call it.

The main difference between REST an MCP is that MCP can maintain state for the current session (that's an option), while REST is supposed to be inherently stateless.

I think most of the other protocols are a variation of MCP.

"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something."

"When disagreeing, please reply to the argument instead of calling names. 'That is idiotic; 1 + 1 is 2, not 3' can be shortened to '1 + 1 is 2, not 3."

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If you know more than others, that's great, but in that case please share some of what you know, so the rest of us can learn. Putdowns don't help.

https://hn.algolia.com/?dateRange=all&page=0&prefix=true&sor...

I was under the impression ACL supersedes KQML and was proposed by FIPA?

any experience with it? illuminate us lot if you will..

I really like this analysis. But what about companies allowing agents to interact natively (via API or similar) getting more of the agents inbound since agents are more optimised to go there? If people want to use agents it will cause a lot of lost revenue for companies not allowing agents to interact natively.

smolagents by huggingface would be more of an agent framework. If it was discussed we would see smolagents/llamaindex/pydantic/etc with frameworks on figure 2. Several frameworks were left off in this paper as it focuses more on the protocols.

https://huggingface.co/docs/smolagents/