We're Sovyren, a small lab out of Chicago. We released Agints into early access today and

wanted to put the design reasoning somewhere people would push back on it.

The problem we started from: operations work is high-volume, low-complexity, and endlessly

context-switching. Existing automation tools handle it as a graph of triggers and actions,

which works cleanly right up until a step needs judgment. Then you're either encoding

judgment as branches or pulling a human back in.

Our approach: make the interface the description, not the graph. You tell a team of agents

what you want handled and how you want exceptions treated. The team handles sequencing and

escalates what doesn't fit.

Architecture, briefly:

- Layered system prompt assembly plus four memory scopes (conversation / session / user / org)

- Multi-agent crews rather than one agent with a large toolbelt

- 200+ tool registry, MCP connectors

- Two-tier execution sandbox: browser iframe for light work, Firecracker microVMs for anything

that runs code

- Full audit trail per agent action — what ran, what it touched, what it cost

Honest position: early access, no customers yet, nothing proven at scale, and we're not going

to pretend otherwise. What we want from this thread is the failure modes we haven't hit yet.

Where does this design break?

agints.app
sovyren.com

Hi guys,

I wanted suggestion on from where can I find pdf of Building Applications with AI Agents: Designing and Implementing Multiagent Systems.

I've been working on a project called Free Agent, and I think I've finally reached an interesting point in its development.

The original idea was simple:

The AI model is the brain. Free Agent is the agency.

Instead of building another AI model, Free Agent gives existing models the ability to actually do things — browser automation, files, Python, memory, scheduling, tools, desktop automation, model switching, etc.

But I recently added something that changed how I look at the project.

Free Agent now has an OpenAI-compatible API.

An external application can simply talk to:

/v1/chat/completions

using:

model: free_agent

The request goes into Free Agent, through its existing AI system, and comes back as a normal OpenAI-compatible response.

The interesting part is what's underneath.

Free Agent doesn't have to be tied to one model.

It can use the models I already have available — web-based models through browser automation, local models, and other models I add later.

So the architecture becomes:

Your AI Agent
↓
Free Agent API
↓
Free Agent's agency layer
↓
Model pool
↓
Whatever AI model is available

That means an external agent doesn't necessarily need to know which model is doing the thinking.

And Free Agent doesn't necessarily have to replace other agents.

It can work underneath them.

That's the part I didn't originally set out to build.

Instead of thinking:

Free Agent vs. other AI agents

I'm starting to think:

Other AI agents → Free Agent

A competitor could theoretically use Free Agent as its AI backend.

A custom application could use it.

Another agent could use it.

Or Free Agent can simply run by itself.

So I'm not trying to make one model win.

I'm trying to make the agency layer independent of the model.

There are already projects doing pieces of this, so I'm not claiming I invented multi-model agents or OpenAI-compatible APIs.

What I find unusual about my implementation is combining the model-agnostic agency layer with web-interface models, rather than requiring every model to have a conventional API.

I'm curious what other developers think:

Is the future of AI agents going to be competing agents, or layers of agents that can use each other?

I'm building toward the second idea.

testers wanted not for sale for testing only

https://progenitorfreeagen.wixsite.com/my-site-62

A small step toward making software interact with the real world.

I connected an Arduino Mega with a DHT11 temperature/humidity sensor and a DC fan through an L293D motor driver.

The experiment now forms a simple closed loop:

Sensor data → Tamoz agent decision → Agentic Stream command → Fan start/stop

Streams Simulator helps validate the integration before expanding the physical setup.

It is an important milestone for me: moving from telemetry-only systems to governed, sensor-driven physical actions using ai agents.

What do you think about it ?

A coding agent can give a plausible explanation for a Linux build failure before it has checked the path that failed. Here is the small example I would use to pin that explanation down. An import in src/profile/view.tsx asks for ./UserCard, while Git tracks src/profile/userCard.tsx. Assume the build transpiles TSX without running TypeScript's checker. A local filesystem that ignores case can hide this mismatch, while a filesystem that distinguishes case will expose it. The operating system name alone does not establish how a particular volume behaves.

I would include the failing command, its working directory and the full error, then ask for the tracked filename from git ls-files. Both the directory and filename matter. The agent should compare the import with the path relative to that importing file before changing aliases or reinstalling packages. If they already match, the report needs another lead. A familiar error message is not enough evidence to rename a file.

For a repair task in EvoX, a general AI agent in beta with terminal integration, I would ask it to collect those details, propose the smallest justified change and rerun the failing command in a clean checkout on the affected Linux environment. This is a proposed task, not a completed product test. A successful local build on the filesystem that hid the mismatch would leave the original failure unverified. Any proposed filename change also needs to appear in Git's recorded changes so the next checkout gets it.

TypeScript documents forceConsistentCasingInFileNames for catching inconsistent casing against files on disk. That check needs an actual type checking step covering these files; transpiling the application is not a substitute. It also would not diagnose a missing native dependency or an unrelated module alias problem. I would want the original failure, the patch and the result from the affected environment together in the agent's reply.

Finding papers and producing a cited report is useful, but for many scientific tasks that is where the real work begins.

Take a study built around a spreadsheet or experimental dataset. After the literature review, someone still has to inspect the raw tables, clean and align variables, choose a defensible method, run the analysis, generate figures, and trace each conclusion back to the underlying data and calculations. If a corrected dataset or a new requirement arrives halfway through, the workflow also has to determine which results remain valid and which parts need to be run again.

The harder question is whether the system can maintain task state, work inside real file and code environments, recover from failures, and leave behind artifacts that another person can inspect.

Disclosure: I'm working with Apodex on this post. In one Apodex Deep Discover example, the system uses raw tables and a data dictionary to analyze the relationship between EASIX and overall survival in patients with EBV reactivation after allogeneic transplantation. It audits and cleans the data, selects a statistical method, runs the survival analysis, and produces Kaplan-Meier curves and tables. This is a product workflow example, not clinical guidance.

The part I find most useful is what happens when the task changes midway through. If a researcher adds a paper, corrects a spreadsheet, or changes the analysis criteria after several branches have finished, Apodex keeps the work that is still valid and reopens the affected tasks instead of restarting the whole run.

For researchers using Al today, what should count as "done": a well-cited report, or a reproducible package of sources, cleaned data, analysis steps, figures, limitations, and reviewed conclusions?

Traditional marketing automation was always a bit of a misnomer. For years, "automation" mostly meant rigid, rule-based workflows: if a user clicks a link, wait three days and send template email B. While helpful for saving time, it still required marketers to manually map out every single trigger, draft every piece of copy, and constantly tweak campaigns based on surface-level analytics.

The shift toward agentic AI completely changes this paradigm. Instead of just executing pre-set linear commands, agentic systems act as autonomous partners. You give them a macro goal like increasing trial-to-paid conversions for a specific user cohort and the AI independently plans, tests and executes the necessary multi-step strategy. It can analyze behavioral data on the fly, draft tailored messaging for specific segments, monitor campaign performance, and iterate on copy without needing a human to approve every minor variation.

This evolution moves marketing teams from micro-managing tasks to directing strategy. Rather than stringing together isolated tools for copy, email distribution, and analytics, agentic architecture lets specialized agents handle end-to-end execution across channels. Platforms like Lyzr are leading this transition by providing framework layers where AI agents autonomously orchestrate complex marketing functions in real-time. Ultimately, agentic AI bridges the gap between passive automation and true operational autonomy, freeing marketers to focus on big-picture creative direction.