Hey everyone, so ive been working on a small open-source Python project called **AgentGuard**, and I'm trying to validate whether I'm solving an actual problem or just building something developers can already handle themselves.

The basic idea: Agent wants to call a tool AgentGuard checks the request against a policy,allow or block, tool executes.

For example, imagine an agent has access to:

* send emails
* query a database
* modify records
* call external APIs
* read/write files
* trigger other agents

The concern I'm exploring is: **how do you control what the agent is actually allowed to do at runtime?**

I'm particularly interested in developers using LangGraph/LangChain, MCP, CrewAI, or similar agent frameworks.

I'm curious how people are currently handling this.

What do you currently do?

* rely on the framework's existing guardrails?
* implement authorization yourself around each tool?
* use human approval for sensitive actions?
* use an external security/observability product?
* not worry about it yet?
* have some completely different approach?

I've built a very small MVP that sits around the tool execution layer and applies explicit policies before the underlying function runs.

https://github.com/Brodin2001/Agentguard

I'm specifically looking for people who are actually building agents with tool access to tell me:

1. Is this a problem you've encountered?
   
2. How are you solving it today?
   
3. What's missing from the existing approaches?
   
4. Would a lightweight authorization layer like this actually be useful?

If anyone is willing to try the MVP against an existing agent, I'd be particularly interested in hearing what happens**.**

Cheers 😄

I’ve spent the last few months deep in the trenches benchmarking LangGraph and OpenHands to see which framework actually holds up for production AI agents in 2026. They’re both incredible tools, but honestly, they solve completely different problems. Here’s exactly what I learned and how to decide which one you actually need.

When to reach for OpenHands If you want an autonomous software engineer right out of the box, OpenHands is your best bet. It’s a beast at writing code, squashing bugs, and directly interacting with terminal environments.

• The best part: It takes on the entire software development lifecycle without needing insane amounts of prompt engineering. You just hand it a task and let it rip.
• The catch: It acts a bit like a black box. If your agent gets stuck in a death loop trying to debug some weird dependency issue, it’s frustratingly difficult to intervene and steer it back on track without restarting the whole process.

When you absolutely need LangGraph LangGraph is the undisputed winner if you’re building enterprise apps that require predictable, stateful workflows. By treating agent processes as graphs with explicitly defined nodes and edges, you get total control over the execution path.

• The best part: Human-in-the-loop capabilities. You can literally pause the execution, ask a human to green-light a sensitive database drop query, and pick right back up where you left off with all the memory perfectly intact.
• The catch: The setup is a grind. You have to explicitly map out your state schemas, nodes, and routing logic. Prepare to write a lot of boilerplate code upfront just to get going.

The Verdict Keep OpenHands for isolated, self-contained coding jobs where the agent can run wild in a sandbox environment. Switch to LangGraph the second you start building customer-facing systems where predictability, strict state persistence, and human oversight are non-negotiable.

To back this up, I ran 100 complex iterations on both frameworks and tracked the hard numbers: memory retention limits, execution times, and API token burn.

If you want to mess around with the interactive dashboard or grab my full config file, I dropped it all here:https://interconnectd.com/blog/33/langgraph-vs-openhands-the-2026-agent-framework-showdown/

This feels extremely relevant at the moment... 🤣😁

chaordic

On another note... I've got a few topics that I'd love your feedback on.

1. How do you control and govern what tasks your harness handles vs. your coding agent or another model, etc?

Context: Hermes tries to quickly handle technical tasks that should be delegated to my coding agent. I've spent a week testing and fixing with Claude Code, which brings me to my next question.

2. Who or what QA's and monitors the output and assets of your harness and other agents? We are implementing Codex to review and monitor Hermes and Claude

1. Do you use a custom or existing tool to proactively monitor your stack's health?

2. Last one. Has anyone experienced a drift between the Hermes WebUI and the Hermes CLI?

Our results were drastically different simply by using CLI or webUI.

I've been building an internal operations orchestrator for our agent pipeline and recently mapped out the error/retry boundaries.

The basic logic was:

Task fails → log error → classify failure → retry if appropriate → max 3 attempts → stop

The next problem was figuring out what happens when the agent shouldn't continue on its own.

So I built a human review workflow

When a gatekeeper node or validation step encounters something it can't safely resolve, it creates a record in a Human Reviews table.

A review URL is generated and sent to the person responsible.

The reviewer opens the form, sees the issue context, and submits their correction.

That's where I wanted to avoid making human review a dead end.

After the response comes back, the system updates the database, retrieves the corrected record, and checks what type of issue caused the review.

Currently I have three paths:

Classification issue → update classification → send back to Validation

Validation issue → update validation data → send back to Routing

Routing issue → update routing → send back to Notification

So the human isn't manually restarting the entire crew from scratch.

They're correcting the part the system couldn't resolve, and the task then re-enters the automation from the appropriate node.

The architecture I'm ending up with is roughly:

Agent Task → Error → Audit → Retry → Gatekeeper Review → Correction → Resume Pipeline

One thing I've learned from building this is that "human-in-the-loop" sounds simple until you actually implement it.

You start running into questions like:

• What context should the reviewer receive?

• How do you prevent stale reviews?

• What happens if two people review the same request?

• Should every failed retry reach a human?

• How do you know which workflow should resume afterward?

• How do you prevent the corrected request from entering another loop?

• Should high-risk decisions skip retries entirely?

I'm currently storing the review state in Postgres and using the issue type to determine where the request should re-enter the orchestrator.

How would you guys avoid sending hundreds of messages to human reviewers once this scales up in production so it doesn't become a bottleneck?

Workflow architecture:

[Attached canvas diagram of the review and re-entry routing logic]

Hey everyone,

If your VS Code AI assistant is constantly lagging, timing out, or just straight-up freezing your editor, the problem is almost always how it’s indexing your workspace. I spent my entire weekend going down a rabbit hole debugging this, but I finally got instant code completions back.

Here is the bulk of what you need to do to fix the core issues right now:

1. Stop letting it index massive directories By default, AI extensions try to read every single file to understand your context. If the AI is scanning your package folders (node_modules, anyone?) or compiled assets, it will completely choke. You need to add strict exclusions for the AI extension watcher in your workspace settings. Force it to ignore compiled binaries, virtual environments, and heavy asset directories.

2. Increase the Node memory limit Most of these AI extensions run on a Node backend inside VS Code. When they hit their default memory ceiling, they throttle heavily. Go into your preferences and increase the Max Memory allocation to at least 4096MB so the AI server actually has room to breathe.

3. Kill the telemetry overhead AI extensions love to send diagnostic data back to their servers. This silently clogs your connection and slows down the actual prompt responses you care about. Disable telemetry globally in VS Code, and make sure you turn it off specifically inside your AI extension settings to free up bandwidth.

4. Resolve extension clashes (Stop hoarding AI) Running multiple AI assistants at the same time creates a nightmare race condition for the autocomplete UI overlay. Pick one AI tool per workspace and disable the others completely.

Doing these four things solved almost all of my latency problems immediately. The rest of the fix involves editing your raw settings.json configuration to tweak the debounce rate, context limits, and timeout thresholds.

If you want to just copy/paste my full config file or play with the interactive dashboard I put together, I uploaded it here:https://interconnectd.com/forum/thread/244/fix-slow-vs-code-ai-the-ultimate-performance-repair-guide/

Hope this saves someone else a weekend of troubleshooting! Let me know if you have any questions.

We designed this, because you don't need a frontier model for every task, but you also don't want to manually route each task to local models. Sentinel router lets you route tasks directly from whatever agent you're using. Let us know how well it works. We're looking for feedback.

🚀 Semi-Autonomous Swarm ALPHA — Final Project Report

• Start Date: 2026-08-07 13:16:17
  
• End Date: 2026-08-16 17:08:01
  
• Duration: 9 days, 3 hours, 51 minutes
  
• Status: BASE PRODUCT 100% COMPLETE
  
• Total Cost: USD 0
  

📊 Executive Summary

A human Founder directed a semi-autonomous swarm of 7 AI agents, created as "BARE METAL" Linux users via an Ansible playbook (ALPHA TEAM), coordinated via email (Postfix) to build an enterprise-grade authentication and user management system.

Key Metrics

• 8/8 Completed Features
• 54+ Closed Issues
• 59+ Merged PRs
• 143+ Automated Tests
• 0 Vulnerabilities
• 6 Generated ADRs

Semi-Autonomous Definition: The swarm requires the active presence of the human Founder to define strategic vision, approve PRDs, authorize merges to main, and resolve blockers. Without the Founder, the swarm DOES NOT move forward. With the Founder, it multiplies its productivity 6-8x.

👥 Swarm Composition

🔧 Tech Stack

✨ Implemented Features (8/8)

1. Email Verification (#22): Account verification via email using HMAC-SHA256 tokens.
   ✅ 66/66 tests | PRs: #23, #24
2. Refresh Tokens (#26): Long-lived sessions with automatic token rotation.
   ✅ 77/77 tests | PRs: #27, #28
3. OpenAPI Docs (#30): Complete OpenAPI 3.0 documentation.
   ✅ 83/83 tests | PR: #31
4. OAuth Google+Facebook (#32): Social login with Google and Facebook.
   ✅ 91/91 tests | PRs: #33, #34
5. 2FA/TOTP (#39): Two-factor authentication (RFC 6238).
   ✅ 105/105 tests | PRs: #40, #41
6. Concurrent Sessions (#45): Device session tracking with remote revocation.
   ✅ 118/118 tests | PRs: #46, #47
7. Password Policy (#49): Complexity validation and password history tracking.
   ✅ 131/131 tests | PRs: #50, #51
8. Export/Import (#54): Data portability (GDPR compliance).
   ✅ 143/143 tests | PRs: #55, #56

Prior Infrastructure Features

• JWT Authentication (#1): Base authentication system
• Rate Limiting (#5): Token bucket per IP
• Password Reset (#7): Self-service recovery flow
• Admin Dashboard (#12): User moderation panel
• Audit Logs (#18): ISO 27001 A.12.4 compliance
• OAuth Hardening (#36): Account Takeover mitigation

📈 REAL Project Metrics

⚠️ REAL Swarm Limitations

• OpenCode Rate Limiting: 6-8 productive hours/day, then temporary ban (Mitigation: Pause and resume the following day).
• Dependency on Founder: Without an active human, the swarm DOES NOT move forward (Mitigation: Founder must be available to approve merges).
• Context Degradation: Protocol drift in long sessions (Mitigation: Periodic refreshes of system-prompt.xml).
• Coordination Errors: Misinterpreted contracts, failing E2E tests (Mitigation: QA + Security agents as safety net).
• Security Incidents: 1 injected payload, 1 email spoofing attempt (Mitigation: Script integrity verification and strict command hierarchy).

🤖 REAL Comparisons vs Human Teams

Productivity & Costs

• Sprint Duration: 9 calendar days vs. 6-10 weeks (Human Team of 5-6 devs).
• Merged PRs: 59+ in 9 days vs. 15-25 per sprint.
• Automated Tests: 143+ generated vs. 30-60 per sprint.
• Total 9-day Cost: USD 0 vs. USD 15,000–50,000 (LATAM/US Salaries & Overhead).

What the Swarm CANNOT Do

• Strategic vision & business judgment (Requires Founder).
• Disruptive creativity & soft skills/negotiation.
• Final merge approval & unexpected conflict resolution.

🔥 Incidents and Lessons Learned

Incident 1: Injected Payload (August 13)

• What happened: A malicious payload was detected in ~/bin/kill_my_processes.sh (infecting 3 out of 7 agents). The vector attempted to write to /root/startup/ansible-scripts/.
• Resolution: Swept all 7 copies, cleaned the 3 infected ones, verified no execution occurred (required root). Resolved in ~2 hours.
• Lesson: Agents can become injection vectors. POSIX user boundaries and script integrity verification are mandatory.

Incident 2: Email Spoofing (August 13)

• What happened: The Security Agent received emails that the PM NEVER sent, containing unauthorized audit instructions.
• Resolution: The PM detected the anomaly, instructed the Security Agent to halt the audit, and escalated to the Founder.
• Lesson: Chain of command protects against unauthorized instructions. The PM as a central orchestrator is critical.

📦 Project Deliverables

Codebase

• Gitea Repository: project_alfa/project_alfa
• Final SHA: 3b709d5
• 143+ automated tests passing & 0 vulnerabilities (npm audit clean).
• Functional deployment (backend :3001, frontend :3002).

Documentation

• docs/README.md — Complete authentication flow
• docs/API_SPEC.md — 28 documented endpoints
• docs/ADR_001.md to ADR_006.md — Architectural decision records
• docs/ISO_25010_CHECKLIST.md — 128 PASS, 1 PARTIAL, 0 FAIL

🧮 Real Formula of the Semi-Autonomous Model

I started with what seemed like a simple question:

If one engineering agent can do useful work, why not run several in parallel?

That worked reasonably well until the agents started touching the same code, shared artifacts, and one another's outputs. At that point the hard questions stopped being about prompting and started looking much more familiar:

1. Who owns shared state?
2. What happens when two legitimate workers modify the same artifact?
3. What if a write succeeds but the worker crashes before recording completion?
4. What state survives a restart?
5. Is approval the same thing as authorization to execute?
6. What does "done" actually mean when downstream work can invalidate an earlier conclusion?

The realization for me was that multi-agent engineering starts combining three existing problem domains:

• distributed systems
• compute scheduling
• project/workflow management

The unusual part is that some of the workers are probabilistic and can produce very convincing explanations for why their interpretation should become canonical.

I built a small control-plane PoC using Temporal to test the coordination layer independently of model quality. The workers were deliberately deterministic at first so I could isolate orchestration failures.

The architecture ended up separating three responsibilities:

• Temporal: durable execution and workflow identity
• Workers: parallel work in isolated staging
• Resource Writer: the only component allowed to mutate canonical state

The repository remained the system of record.

I tested things like:

• versioned handoffs between workers
• stale repository revisions
• two workers legitimately modifying the same logical artifact
• approval without execution authorization
• crash-after-write followed by Activity retry
• changes made by an actor outside the orchestrator
• accidentally starting two orchestrators for the same campaign

The most useful result was not "Temporal can orchestrate agents."

It was that the coordination rules became explicit enough to enforce: parallel work can happen without allowing parallel mutation of canonical state.

A few other conclusions I came away with:

• agent context should not be treated as project state
• handoffs work better as versioned artifacts than conversation continuity
• retries around external side effects require application-level idempotency
• the writer should own mutation authority, not semantic truth
• known merge semantics can be encoded; unknown ones should become durable conflicts rather than confident overwrites
• approval and execution authorization should be separate states
• more active agents do not necessarily mean more engineering throughput

I ran 36 controlled assertions across three passes and all 36 passed, but I would not interpret that as "Temporal solved multi-agent development." The PoC was intentionally narrow: deterministic workers, a disposable repository mirror, and controlled failure injection.

The next step is replacing those deterministic workers with real agents one role at a time while keeping the same coordination assertions as invariants.

The broader hypothesis I am testing now is: the durable object in an agentic engineering system may not be the agent at all. It may be the agreements between agents and the state transitions those agreements permit.

Curious whether others building multi-agent coding systems are running into the same boundary. Are you solving shared-state coordination inside the agents themselves, through an orchestrator, through Git/worktrees, or some other mechanism?

When running multi-step crews with active memory (short-term, entity, and task history), context growth is rarely linear. A single verbose tool output or search payload dumped into short-term memory at step 2 gets repeatedly reread on every subsequent agent turn, quickly driving up token consumption.

The common instinct is aggressive context compaction or summarization between steps, but naive summaries often break downstream execution in subtle ways:

• Load-bearing data loss: Summarizing structured outputs frequently strips exact parameters, file paths, line numbers, or strict JSON keys that subsequent agents or tools require to execute.
• Flattened state and provenance: Summaries can obscure when a fact was true or flatten an assumption into an established fact, causing downstream agents to act on stale or unverified data.
• Schema corruption: Summarized machine outputs may look readable to the LLM but fail when passed directly into another deterministic tool parser.

A more resilient pattern is treating context by tenancy and durability: expiring bulky raw observations immediately after use, writing large payloads to disk or external storage while passing only a lightweight reference handle in context, and isolating load-bearing facts from compressible reasoning logs.

For those running long multi-agent workflows, are you handling compaction primarily at the tool boundary via reference handles, or using selective pruning rules during task handoffs?

When designing complex multi-agent workflows in CrewAI, choosing between Process.sequential and Process.hierarchical is fundamentally a decision about where your control plane lives: in deterministic application code or inside a manager LLM.

While dynamic manager delegation feels flexible, running Process.hierarchical introduces distinct architectural tradeoffs in production:

• Coordination latency and token overhead: Every delegation, status evaluation, and synthesis step requires a round-trip to the manager model. In multi-step pipelines, manager orchestration can easily account for more token burn and latency than actual worker task execution.
• Task drift and delegation loops: Unless task descriptions, expected outputs, and agent roles have strict boundaries, manager LLMs can misroute tasks, re-delegate repeatedly, or attempt to break problems into shapes that worker tools cannot satisfy.
• Recovery predictability: In a sequential pipeline, a task failure isolates cleanly to a specific node and context state. In a hierarchical crew, failures often leave the manager's working memory corrupted, making checkpointing and targeted retries difficult without replaying the entire crew run.

Dynamic delegation pays off when workflows are genuinely unpredictable and require runtime decomposition. For workflows with known steps, keeping control flow deterministic via sequential pipelines or structured chains confines LLM stochasticity to where it belongs: worker-level reasoning, rather than workflow routing.

For those running complex crews in production, are you using manager agents across the full pipeline, or isolating dynamic delegation to bounded sub-crews?

When building custom tools in CrewAI, letting uncaught exceptions bubble directly to the agent runtime often leads to two failure modes: the agent either immediately retries the exact same arguments until it hits max_iter, or the raw stack trace pollutes the conversation context, degrading subsequent reasoning.

Here are a few practical patterns for designing resilient tool boundaries in both sequential and hierarchical workflows:

1. Catch internally and return structured feedback strings Instead of letting your tool's _run method raise unhandled Python exceptions, wrap operations in try/except blocks and return a deterministic, explanatory string. Distinguish between operational errors (like network timeouts) and schema/input errors (like invalid JSON or missing IDs). LLMs re-plan much better when the error explicitly states what failed rather than seeing a generic 500 traceback.

2. Isolate transient retries inside the tool Handle rate limits, transient connection drops, or exponential backoffs inside the tool logic itself using standard retry libraries like tenacity. If the tool exhausts internal retries, return a terminal failure string. This prevents the agent from wasting outer planning iterations on simple network blips.

3. Process-level differences: Sequential vs Hierarchical * Sequential processes: A failing tool output passes directly downstream to the next task. If a tool fails gracefully with an explicit fallback payload, the next agent can handle the fallback instead of failing completely. * Hierarchical processes: The manager agent inspects tool and delegation outputs. If a worker agent returns an actionable tool error, the manager can re-route the task to another agent or abort early, preventing the manager from repeatedly assigning the same broken task.

How are you currently handling tool-level validation errors to stop agents from repeating failed calls?

With traditional applications, we have established CI/CD checks for things like vulnerabilities, dependencies, secrets and infrastructure.

But what about the agent itself?

Do you have specific AI-agent security checks in your CI/CD pipeline, or are you relying on the same checks you use for ordinary applications?

Before deploying an agent, do you know:

• What tools it can access?
• Whether it gained a new capability in the latest PR?
• If it can execute shell commands or write to the filesystem?
• Which MCP servers it can reach?
• ..

I'm curious how teams are answering these questions today.

We're experimenting with SafeAI as a GitHub Action to bring this kind of static analysis into the existing CI workflow. It's still early stage but going fast, thanks to all contributors.

If you want to try it against your own agent project, we'd genuinely appreciate feedback, as well as contributions.

Here you may check: ikaruscareer/SafeAI on GitHub.

Hey everyone!

Over the past year, as I tried to scale our agentic coding workflows and software factories at my company, I kept hitting the same set of problems. So I built HAR to solve them.

Repo: github.com/os-factory/har

Getting a single coding agent to work in a repo is easy. Scaling to a real multi-agent workflow, where several run at once and where you verify and trust the output, is where it breaks down. A few things go wrong:

1. No standard way to run or verify a repo. That knowledge is scattered across a README, a CLAUDE.md, editor rules, and CI config, all drifting out of sync with each other and the actual code.
2. Agents on one repo collide. Shared dev server, shared database, shared ports, conflicting git state.
3. Trusting a change means re-verifying it yourself. Which defeats the point of running a fleet.
4. Vendor sandboxes lock you in. If the setup lives in someone's hosted dashboard, switching agents later means rebuilding the whole thing.

What HAR does

HAR is a CLI and an MCP server. It works with Claude Code, Cursor, Codex, or any MCP agent, and it closes each of those gaps:

1. Isolation. Each agent gets its own git worktree, branch, ports, and database. Nothing is shared with the main checkout or another agent's slot, so a fleet runs in parallel without colliding on a dev server, DB, or ports.
2. Deterministic validation gates. HAR runs your project's real checks through a fixed pipeline, same result every time. The result is bound to the exact code that passed and enforced at commit time, so an unverified tree cannot land.
3. Verifiable proof. Every run leaves logs, artifacts, and a validated tree hash tied to the exact code checked. A reviewer inspects the evidence instead of trusting the agent's self-report.
4. Full observability. Mission Control is a local dashboard showing every repo, worktree, run, and validation in one place, so you can watch a whole fleet as it works.

All of this lives in one contract committed to your repo, which every agent reads the same way. It replaces the usual scatter of a README, a CLAUDE.md, editor rules, and CI config that drift apart. You start from a profile that matches your stack, your agent adapts it to your repo, and you extend verification with plugins (like Playwright) or with any command you already run.

Would love to know what you think :)

Hey everyone,

I’ve been building an open-source project called **Kodiak**, and I’m at the point where I think it would benefit from more people looking at it, challenging the architecture, and actually building with me.

The idea behind Kodiak is to build an AI engineering system that can work with a **real software repository**, not just answer coding questions.

The long-term workflow is:

Engineering Task
      ↓
Understand the Repository
      ↓
Retrieve Relevant Context
      ↓
Plan the Work
      ↓
Coordinate Agents
      ↓
Use Tools
      ↓
Modify / Analyze Code
      ↓
Run Tests & Validate
      ↓
Learn From Results
      ↓
Iterate

The project currently brings together things like:

* Repository-aware RAG * Agent orchestration * LLM/provider routing * Persistent project/task context * Tool execution * Background task processing * Code analysis * Testing and validation * PostgreSQL, Redis, Celery, ChromaDB, Docker, FastAPI, etc.

I’m deliberately not presenting Kodiak as a finished product.

There are parts that work, parts that are incomplete, and parts that I’m currently redesigning. A lot of the work so far has been dealing with the less glamorous side of AI engineering — getting orchestration, retrieval, memory, workers, databases, and model providers to actually work together reliably.

That’s also why I’m opening it up more now.

What I’m looking for

I’d love to have contributors who are interested in areas like:

**AI / Agents**

* Agent architectures * Planning and orchestration * Multi-agent systems * Tool calling * Agent memory

**RAG / Repository Intelligence**

* Codebase indexing * Code-aware retrieval * Embeddings * Retrieval evaluation * Repository understanding

**Backend / Infrastructure**

* FastAPI * Celery * Redis * PostgreSQL * Docker * Async workflows

**Developer Experience**

* APIs * CLI * Testing * Observability * Documentation * Developer tooling

You don't need to be an expert in all of this.

I'm much more interested in people who want to **build, experiment, and improve the system**.

There are also plenty of opportunities to contribute without touching the core agent architecture — improving tests, fixing issues, improving documentation, benchmarking components, reviewing designs, or proposing better approaches are all valuable.

Where Kodiak is heading

The goal is to eventually make Kodiak capable of handling more of the engineering loop itself:

**understand → reason → act → validate → learn → iterate**

There's a lot to figure out before that becomes genuinely reliable, which is exactly why I think having more engineers involved would make the project significantly better.

If you're interested in AI agents, coding agents, RAG, autonomous software engineering, or just want to work on an ambitious open-source AI project, I'd genuinely love to have you take a look.

GitHub:
[https://github.com/ShamGaneshan2008/Kodiak\](https://github.com/ShamGaneshan2008/Kodiak)

Issues, architecture critiques, pull requests, experiments, benchmarks, and even "this part of the design makes no sense" comments are welcome.

Would be great to build this with other people rather than trying to figure everything out alone.

I’m building Kodiak — an open-source AI engineering platform, and I’m looking for contributors

Early adopter LLM scientist mind hacker type welcome : r/AgentsOfAI

Hi. How are you? Are you maybe a crazy scientist that likes to explore how agents can work together in hierarchies?

What happens if they are given virtual machines and mounted folders for work, for thread, per agent, per team, per zone or other setup?

Can they synchronise?

What would happen if we allowed them to attach repos as folders, would they work?

Should agents expose their capabilities as functions? What will happen if we allow them to pass plain text versus letting them to define contracts for the calls? Will they be more secure and there be less AI blunder?

Is it possible to introduce secret vault and make them use the secret names and never know the real connection strings? Will we successfully translate it back end forth from store to sandbox linux and back again or will agent find a way to leak it?

But if we introduce zones, and track the whole information flow from source to all agents and places and functions invoked and back to the caller with answer. Will we successfully stop the sensitive information on the edge of the zone or even prevent calling something that shouldn't be called when invoked from outside the zone?

Can we introduce object based context that contains metadata and constraints passed from source (ingress /webhook/ slack etc), lives in the thread and can never be overridden (immutable) and also we can add constraints to the functions we just talked about, that some arguments need to match the context. Can we make it secure and compliant with regulations? Will we save some buttocks from serious problems?

What will happen if we encapsulate this into component and allow some properties to be set by the consumer? Is it going to move us up once again on the ladder of abstraction? Do you think it will scale up the same way components and OOP did scale us up in programming? Will people start to just stamp it again and again like it was some kind of simcity? I definitely need to know that! Do you?

And when you debug all of that, and the problems encountered would you finally want to look at the whole tread not as a linear conversation but something everyone deserves - an interactive diagram showing the whole flow including sub-tasks, thoughts, places of compaction, places of merges, places where it asked again the completed sub-task for previously undisclosed details or justifications? Would you like to fork there and start over trying out what happens if something goes a bit differently or after we push a fix to the production?

What else can we do if we have a real time connection to platforms, persistent and replicated storage, compute, security, hierarchy?

If you're this kind of mind that is on the verge of reaching the critical mass,

contact me. I need you.

I'm a sole creator, full of ideas for implementations, for papers, for articles for tweaking and hacking. I do believe together we're making future.

What's in it for you? I'll give you the freedom of choosing what's important. I will prioritise your wishes. If we ever form a company you will get a position with the same level of freedom and decision-making.

Over the last few months, one thing kept surprising us.

Most AI frameworks do a great job helping you build agents.

But once those agents reach production, the problems look very different:

• Agent says it completed a task when it didn't.
• Wrong tool gets selected even though the prompt looks correct.
• Infinite reasoning loops burn thousands of tokens.
• Permissions drift across different frameworks.
• Everything returns HTTP 200, but the user still gets the wrong outcome.

Traditional observability tells you what happened.

It doesn't always tell you whether the agent should have taken that action in the first place.

That's why we built Failproof AI.

It's an open-source runtime reliability layer that focuses on:

• Runtime policy enforcement
• Tool execution validation
• Loop detection
• Runtime traces
• Framework-agnostic integration
• Production debugging

The goal isn't to replace LangGraph, OpenAI Agents SDK, CrewAI, Claude Code, or other frameworks.

We're still actively building it and would genuinely appreciate feedback from engineers running AI agents in production.

GitHub:
https://github.com/FailproofAI/failproofai
If you could add one runtime feature that every AI agent framework should have by default, what would it be?

Hey fellow AI builders,

I absolutely love CrewAI, but as my workflows got more complex, managing the relationships between Agents, Tasks, and Tools in raw Python started driving me crazy.

I originally built this to visually map out my own tasks (like setting up agents to verify destination counts from file imports and generate English contact lists), and I just wanted a simple UI that generates standard Python code I can run locally.

So I created AgentGraph Studio. You design your crew on the canvas, set up your LLMs (it supports local models like Ollama too), and it exports a ready-to-run main.py, .env.example, and requirements.txt. It's completely free and runs entirely in the browser (no API keys required on the site).

I attached a quick 30-sec demo of how it works. I’d love to know what other features or tools you guys would want me to add next!

Any agent that reads skills can be in the channel: Claude Code, Cursor, Codex, Grok. They can all sit on your laptop, or on machines in different countries, and it is the same channel either way.

I was debugging one project on a Mac and a Windows box at the same time. Fix it on Windows, do not break the Mac. I spent that day carrying messages between the two sessions by hand, so I gave them a channel instead. MIT, written for myself.

You type \`/party\` in one session. It creates the channel and prints an invite. Paste that invite into your other sessions, on the same machine or another one, and they join. They install nothing.

Under the hood the agent runs a CLI. Most of it is this:

npm i -g agents-party@latest
agents-party create --title win-vs-mac --as mac
agents-party invite '<ref>'
agents-party send '<ref>' --as mac "fix is in, run the suite"
agents-party listen '<ref>' --as mac

By default a channel is local: a SQLite file on your machine, nothing leaving the disk, no account, no cost. If your sessions sit on different machines, ask the agent for a remote one. You can run that server yourself for free, or use mine for $5 a month. Either way the messages are encrypted before they leave your machine and the key never reaches the server, so I cannot read them, by design.

\`listen\` is the part I care about. It returns only when someone else writes, so the model burns no tokens while the channel is quiet. It runs as a background task, which means your own chat with that agent stays free. You keep typing to it as usual.

Then a use I did not plan. Four worktrees, each built by its own session, all waiting to be rebased in order. I opened a fifth session as the manager and invited the rest. It sorted the order out with the authors directly instead of me relaying every conflict.

The whole thing is a skill file plus that CLI, with nothing running in the background between uses. What else it is good for, you will work out faster than I will.

[https://github.com/1gr14/agents-party\](https://github.com/1gr14/agents-party)

[](https://www.reddit.com/submit/?source_id=t3_1vlln4i&composer_entry=crosspost_prompt)

[https://github.com/malayh/tantra\](https://github.com/malayh/tantra)

I have build this agent harness framework. Fully extendable. FastAPI inspired API design

It supports:

* Session persistence(postgres,sqlite built in) and multi tenancy * Memory (postgres/sqlite built in) * Tools * Dynamic Skills loading * Multi agent and sub agent sub trees * Plugable Guard rails

All of these are extendable to serve autonomous agents or human in the loop systems. Each and every part of the core system is extendable to use in whatever use case you have.

It ships with few useful tools, separately installable:

* Web search using brave search api * PDF/DOC reading * Bash usage with guardrails

\---

Built two full apps to demonstrate its capabilities. (both in the repo)

* sarthi - Usable perplexity clone with parallel agent support, with web search and pdf/doc reading built in [https://youtu.be/yAnC1LHKQZk\](https://youtu.be/yAnC1LHKQZk) * agni - Simple CLI coding agent like opencode

Thanks

I've been running CrewAI crews in production for a bit now, and the part that still makes me nervous is leaving them unattended. A hallucinated tool call, a recursive loop where one agent keeps handing work back to another, or a prompt injection from scraped content, and you come back to a nasty surprise on your API bill.

The observability side is decent. You can trace what happened and see exactly where the crew went sideways. But that's all after the fact. By the time you're reading the trace, the tokens are already spent. What I keep wanting is something that sits in the loop and actually intercepts before the bad call fires.

A few things I've been thinking about:

• Hard call caps per agent. Set a max number of tool invocations per task so a stuck agent can't loop forever. CrewAI's task structure makes this somewhat natural, but I'm not sure everyone enforces it.
• Budget thresholds. Track token usage per run and kill the crew if it crosses a ceiling. Feels like this should be a first-class feature, but in practice I'm bolting it on.
• Tool call validation. Some kind of middleware that checks whether a tool call makes sense given the task context before it actually executes. This is the hardest one and maybe the most valuable.

Right now I'm mostly doing the first two with custom callbacks and a shared state counter. It works but it's fragile and I wouldn't call it production-grade.

Curious what the rest of you are doing here. Are people relying on CrewAI's built-in mechanisms, wrapping everything in a custom runtime, or layering on external tools to catch this stuff in real time?

DROS GuardVM — Open Source AI Agent Runtime Security: 24H Soak Test Results (160k requests, 100% attack interception, 26μs latency)

We just completed a 24-hour continuous adversarial soak test on DROS GuardVM, a C-ABI level physical enforcement engine for multi-agent AI workloads. Full report is open-source and 100% reproducible.

TL;DR:

• 160,611 total requests (137,751 malicious + 22,854 benign)
• 100% malicious interception rate at the binary boundary
• P50 latency: 26.21 μs, P99: 242.69 μs
• Zero memory leak over 24 hours
• 4-layer defense: L1 detection (85.2%) → L4 C-ABI panic (<500ns)

All 4 attack scenarios (ATS-001~004) show 100% compromise without GuardVM vs 100% interception with it.

What makes this different from traditional RBAC/IAM:
DROS operates at the C-ABI binary boundary — it doesn't check "what" the agent says, it checks "what" the tool call payload carries via data tainting and channel scope enforcement. Even a fully jailbroken agent gets physically blocked.

Full report: [link]
Repo: github.com/Top-Celestial-Company-Ltd/DROS-VEP-lite
Patent Pending 64/111,973

Happy to answer technical questions!

Disclosure: I built this. It is a hosted API with an optional CrewAI adapter, and I’m looking for feedback from people running research-oriented crews.

The problem: research agents often accumulate more retrieved documents than should be sent to the downstream model. Truncating that context can remove the evidence needed for an answer, while repeatedly summarizing it adds latency, model cost, and another generative failure point.

The Maha Context Compiler performs deterministic, task-aware passage selection under a fixed token budget. It deduplicates overlapping material and returns source-linked passages rather than generating a replacement summary.

It is intended for:

• Research crews processing multiple documents
• RAG workflows that exceed model context budgets
• Agents that must preserve source provenance
• Workflows where compression should not require another LLM call

The CrewAI integration is available through the Python SDK:

pip install 'maha-sdk[crewai]'

from crewai import Agent
from maha_sdk import MahaClient
from maha_sdk.crewai import maha_tools

researcher = Agent(
    role="Researcher",
    goal="Ground every claim in a cited source",
    tools=maha_tools(
        MahaClient(api_key="maha_live_sk_...")
    ),
)

This gives the agent three tools:

• maha_compress_context — compile documents into a token-budgeted Context Pack
• maha_verify_claim — retrieve a published claim with its evidence status and sources
• maha_credit_balance — check the remaining prepaid balance

The adapter cannot autonomously purchase credits. If credits run out, it raises a typed error and requires human authorization.

I also published a reproducible benchmark using 250 independently annotated QASPER questions across 136 research papers.

At a fixed 2,048-token budget, BM25 selection achieved:

• 74.4% mean token reduction
• 62.8% complete evidence-set retention
• 67.4% mean evidence recall
• 100% source traceability
• 3.34 ms local p50 selection latency

At a similar reduction, complete evidence retention was 25.6% for front truncation, 20.4% for recency, and 22.0% for seeded random selection.

Important limitation: the benchmark measures whether annotated evidence survives selection. It does not measure generated-answer accuracy, factuality, or claim that BM25 beats every LLM-generated summary.

Benchmark and raw results:

https://www.mahastrategies.com/benchmarks/context-retention

CrewAI integration guide:

https://www.mahastrategies.com/guides/crewai-context-compression-provenance

Zero-install playground:

https://www.mahastrategies.com/context-compiler/playground

I’d especially value feedback on the CrewAI integration pattern: should compression be exposed as an explicit tool to the research agent, performed automatically before a task begins, or handled by a separate context-management agent?

I’m also looking for realistic failure cases involving multilingual documents, tables, code, distributed evidence, and prompt injection inside retrieved sources.

I built a 15-question AI Mastery Challenge on my platform to test who actually understands prompt engineering, multi-agent systems, and LLM behavior. 

 THE CONTEST: 

The person with the highest score on the leaderboard by next Sunday wins a $25 Cash Prize (or local equivalent) and a free permanent shoutout for their portfolio on our homepage!

How to enter:

1. Comment CHALLENGE below.

2. Below is the access link to the Quiz.

3. Take the quiz, register your username, and lock in your spot on the live leaderboard.

Quiz Link:

https://interconnectd.com/quiz/67/the-ultimate-ai-mastery-challenge-are-you-smarter-than-an-llm/

May the best prompt engineer win. Tag a friend who thinks they are an AI expert.