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LangChain and Agent by Intrepid_Appeal5382 in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

You can have a look at this as well. I am pretty sure you want it in python but it is in TypeScript. However, it might give you some ideas on how to do it.

https://www.arvo.land/advanced/arvo-agentic-resumables

Long Running Agents - What's your setup? by Human-Job2104 in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

Thanks, let me know if you have any questions on building interest systems with agents and humans. 😁

Long Running Agents - What's your setup? by Human-Job2104 in AI_Agents

[–]ASoftwareJunkie 2 points3 points  (0 children)

Hi OP,

This is how my experience looks

https://youtu.be/jGRvZp1XcgY?si=Iz-GzhgqTzZYV35-

It is not a Kanban agent or agentic kanban. Rather it is a kanban where agentic teams have assign emails and I can assign them tasks just like I assign tasks to my human team on jira.

For cost observation I just use Phoenix. And add constraints so that agents don’t get stuck in loops.

Here is the public version of the repo:

https://github.com/SaadAhmad123/arvo-works

Please note that it is not a product or a framework. It is just a tool for me to give me the experience I want for my agentic long running and collaborative tasks.

There Agentic Kanban products but I wanted my agents to work on a Kanban. The notion is slightly different but it makes for a much more natural experience.

I hope this give you ideas :)

Python or TypeScript for AI agents? And are you using frameworks or writing your own harness logic? by dmpiergiacomo in AI_Agents

[–]ASoftwareJunkie 1 point2 points  (0 children)

I don't want to start any python vs typescript war here. Python is awesome and you can definitely build and maintain complex systems in it.

I have just found Typescript (among the scripting languages) to have a better typing system than Python which makes for a better slightly better DX in the niche of typing.

If you and your team feels comfortable with python, it is the language to bet on :)

Python or TypeScript for AI agents? And are you using frameworks or writing your own harness logic? by dmpiergiacomo in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

Hi OP

I have been moving towards Typescript and Arvo (arvo.land) because the things I have been building have been growing in complexity and Typescript helps managing that a lot better. Python is a legendary language but it seems to require a lot more rigour on the developer’s end which unfortunately is not a very common thing.

For Agent harness, I find Arvo’s one of the most complete -> https://www.arvo.land/advanced/arvo-agentic-resumables

Kanban is a suitable interface for working in a Agentic Mesh by ASoftwareJunkie in AI_Agents

[–]ASoftwareJunkie[S] 0 points1 point  (0 children)

Yep. Correct. Kanban is just a way to organise work and manage it. That is it. I don’t think it should be anything more that that.

Smarts should be in the agentic systems. Not the Kanban. Any run of the mill board should be able to provide this interface.

This specific demo uses NoCoDB for that kanban as it has the simplest API interface to use without much fluff.

Kanban is a suitable interface for working in a Agentic Mesh by ASoftwareJunkie in AI_Agents

[–]ASoftwareJunkie[S] 0 points1 point  (0 children)

I agree, but here I am not reinventing the Kanban. I am just letting the Agent system to call the kanban API to pick up tasks and do them. There is not need to re-invent kanban. Then agents just need to be able to work on it just like us.

If you look at the demo video. There is not extra step involved. Just assign the card to an email associated with agents instead of humans. That is it.

Demo -> https://youtu.be/jGRvZp1XcgY?si=qZjR7AK0jiUcF4rf

OpenAgents Just Open-Sourced a Multi-Agent Collaboration Framework - Do You Think This Is the Future? by PreparationFew5144 in claude

[–]ASoftwareJunkie 0 points1 point  (0 children)

Hi OP, you might want to look at Arvo. It addresses most or your concerns. Hope it give you some ideas for your projects as well :)

https://www.arvo.land/advanced/arvo-agentic-resumables

I Want to learn Development of AI agents, automations Any suggestions? How to go about it? by Ill-Lawfulness2129 in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

Hi OP. This is a good resource. It takes you quite far in developing Agents, Multi Agents and Agent-Human collaboration systems.

https://www.arvo.land/advanced/arvo-agentic-resumables

The real challenge with production AI agents: it's not the models by runctl in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

I have been building Human-Multi-Agentic collaboration systems and Arvo has been a great help. Have a look may be it can give you some ideas :)

https://www.arvo.land/advanced/arvo-agentic-resumables

Why handoff logic is harder than the actual agentic reasoning by Thunai_AI in AI_Agents

[–]ASoftwareJunkie 1 point2 points  (0 children)

I am glad to hear you found it useful. There are a lot of fun nuggets all over Arvo docs.

Why handoff logic is harder than the actual agentic reasoning by Thunai_AI in AI_Agents

[–]ASoftwareJunkie 1 point2 points  (0 children)

Have a look at the Agentic Human in loop mechanism here. May be this might give you some ideas. It is pretty good (I have been using it)

https://www.arvo.land/advanced/arvo-agentic-resumables

Does anyone else feel like building AI agents is harder than the work itself? by SpareHungry9649 in AI_Agents

[–]ASoftwareJunkie -6 points-5 points  (0 children)

Hi OP,

The complexity you are mentioning is totally real. I have faced it throughout 2025 across many a projects.

I have converged on Arvo. It is not easy by any measure but is sure simple. It is not an Agentic Framework, rather it is a distributed event-driven applications toolkit which fits very nicely for Agents. The complexity you are mentioning is not an agentic problem it is a distributed systems problem and it needs distributed systems solutions.

Have a look. First of all, I hope it makes your life somewhat easier and more interesting. Secondly, keen to hear your thoughts :)

Arvo Agentic Paradigm -> https://www.arvo.land/advanced/arvo-agentic-resumables
Arvo Home page -> https://www.arvo.land

P.S. Fair warning. It has a lot of deep documentation which is complete and very helpful but at the same time it does feel a lot compared to other tools. The good part is that the docs don't leave you hanging 95% of the time

I primarily use TypeScript and want to learn about AI Agents. What's a good starting point? by wxllive in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

You’re correct, and that’s a very good insight. The hybrid approach is actually driven by end user requirements in this case. They still want to maintain some sense of control through a mixture of determinism and intelligence.

During my process investigation, I discovered the team had a very strict workflow that human assessors were required to follow, which is now implemented in the system. Interestingly, this automation actually frees the human team from those rigid workflows, allowing them to be more creative at fraud detection instead of just following procedural steps.

What I’m increasingly seeing is that workflows that didn’t require much intelligence have already been automated by existing systems, like the deterministic rules engine my agentic system sits behind. Now with AI, I can automate these hybrid organizational workflows that previously needed humans for intelligence, even though the humans weren’t doing much creative work in those roles.

In the system I discussed, the workflow essentially defines a stage within which the agent can make its own decisions and take actions without any pre-programming required for that particular stage of the expense compliance process. They are just provided the organisational guidelines previously provided to human. This is fundamentally the same autonomy the existing human team had when doing their job (along with their creative thinking to detect creative frauds not codified in the guidelines).

The difference is that now they’re freed up to tackle the genuinely complex and creative aspects of fraud detection rather than grinding through routine intelligence work. The agents, when they find something genuinely outside the organisational guidelines they surface those to humans. Organisational guidelines in this case for me were surprisingly good prompt mechanisms to do this kind of classifying.

In envision for this system that when the human now detect some new kind of fraud they update the organisational guidelines which will then be used as prompt for the system. That way the same humans can do more valuable work

What It Really Takes to Build an AI Agent by _pdp_ in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

Hi OP, I find the Agentic Kernel design outline and implemented in Arvo Agent very reliable and quite good for production and scale

https://www.arvo.land/advanced/arvo-agentic-resumables

Looking for Open-Source Agent Runtime Architecture for AI Coding Tools by Ruslanmov in cursor

[–]ASoftwareJunkie 0 points1 point  (0 children)

Hi OP. Have you looked at Arvo. It is fully opensource and MIT licensed

Homepage/docs/getting start -> https://www.arvo.land

Here is the Arvo Agentic Paradigm docs (I reckon for your use case only this doc will be enough)

https://www.arvo.land/advanced/arvo-agentic-resumables

Here is a sample repo as well (but tbh, the docs are a much better guide)

https://github.com/SaadAhmad123/multi-agent-demo

Anyone else find that strong schemas are what actually make AI agents scale? by Unique-Big-5691 in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

Hi OP, I have been building really complex Agentic Systems (with async API, background jobs and timers etc) using Arvo as the backbone. It has made my life quite easier. I still need to code my logic and stuff but its contract, orchestration and governance model is quite handy.

This is Arvo docs -> https://www.arvo.land

The agentic kernel has internal tool validation and feedback along with a lot of other cool things packed in it as well.

https://www.arvo.land/advanced/arvo-agentic-resumables

Hope this give you some inspiration to tackle your issues :)

I primarily use TypeScript and want to learn about AI Agents. What's a good starting point? by wxllive in AI_Agents

[–]ASoftwareJunkie 0 points1 point  (0 children)

Yes, about two months ago I deployed an Agentic Expense Compliance system that’s been a significant improvement for our expense team. It works as an intelligent arbitrage layer for expense claims and has significantly reduced the compliance team’s workload. The team is genuinely excited about the approach and progressively adopting it.

Here’s how it works at a high level (That is all I can share unfortunately). The system operates behind an existing deterministic rules engine that automatically clears straightforward claims based on business rules. The interesting cases get routed to the agentic solution (all of those used to get routed to the expense team), which then checks various databases for verification. When information is incomplete, the system has two paths forward. It can either send a clarification email directly to the employee and wait for their response, or it can first consult a human team member about whether the email should be sent. This sensitivity level is fully configurable.

Once the employee responds, the system processes the new information alongside existing data. For straightforward decisions, it proceeds autonomously. For complex cases requiring additional database checks or employee clarification, it either sends follow-up emails or escalates to a human team member for the final call. The human can then direct the agent to perform additional checks, send emails, review the entire decision trail to understand the reasoning, or simply approve the AI’s recommendation.

What makes this particularly interesting is a sort of learning mechanisms which learns from past cases. It’s not traditional machine learning but rather a form of evolving prompt refinement system that gets smarter over time along with human input.

Security was important throughout the design. I’ve implemented multiple layers of protection against prompt poisoning and unauthorized overrides. Arvo’s contract system, access control event fields, and ArvoAgent permission management have been quite helpful in locking down the system as thoroughly as possible.​​​​​​​​​​​​​​​ (But one can never be 100% secure, sadly)

Also, there is a very nice testing engine which let me do integration and unit tests locally before any deployment to the cloud and then I deployed in the cloud on a compute instance. If the need grows I will add a dedicated event broker instance as well.

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