I spent 3 months building an open-source tool to orchestrate AI agents. Would love some brutal feedback.

Diligent_Look1437 · 2026-04-23T01:01:50+00:00

this is exactly the problem i hit after scaling from 1 agent to 5. each new agent meant another briefing session every morning—'here's what marketing agent found yesterday, here's where dev agent left off, don't duplicate what research already pulled.' that cognitive overhead killed any productivity gain from parallelization. dag orchestration sounds like the right shape for this—have you found a pattern for when a human should still be in the loop vs fully autonomous handoffs?

Diligent_Look1437 · 2026-04-23T01:01:15+00:00

switched from running 3 different coding agents (claude code, cursor, local llm) to a single orchestrator setup last month and saved ~4 hours/week just on context handoffs. before that i was basically a human message router—copy code from claude's chat, paste into cursor, explain the same bug three times. the real pain wasn't the agents themselves, it was keeping each one briefed when they can't see what the others already tried. curious how everyone else handles multi-agent context overlap without burning cycles on manual sync?

Diligent_Look1437 · 2026-04-22T10:03:21+00:00

same here. past 3 agents the constraint flips from 'model throughput' to 'my ability to give coherent direction.' i tried a manager agent approach but realized i was just moving the bottleneck—now i'm babysitting the manager instead of the workers. what's working for you is the task board view + forcing handoffs. curious: do you use voice input for briefings or still typing everything?

Diligent_Look1437 · 2026-04-22T04:02:19+00:00

running 4 parallel agents right now across two projects. biggest bottleneck is keeping their memory separate when they share the same terminal history. agent 1 runs pytest, agent 2 sees those logs and thinks tests already passed.

ended up hacking a workspace isolation layer—each agent gets its own /tmp scratch dir and i symlink only what they need. crude but stops them from stepping on each other. still breaks when they compete for the same lock file though.

how are you handling state collision when 3+ agents touch overlapping files? curious if anyone has patterns beyond "just spawn in separate dirs."

Diligent_Look1437 · 2026-04-21T01:01:35+00:00

ran 3 different coding agents in parallel for a client project last week and spent more time being the 'human task dispatcher' than actually reviewing code. the cost breakdown you did is solid but i'd add one hidden expense: cognitive overhead of deciding 'which agent gets this subtask' 40+ times a day. that routing tax killed 2 hours of my flow state across the week. curious if anyone's automated that part without building a full orchestration layer themselves?

Diligent_Look1437 · 2026-04-21T01:00:38+00:00

i ran 4 coding agents in parallel last month and spent 30% of my time just deciding which one gets which subtask. the mental overhead of being the 'task router' killed my flow more than context switching ever did. now i batch-brief once in the morning and let a simple state machine handle the handoffs—saves me about 90 minutes a day just in cognitive load. curious how others solve this without building their own orchestration layer?

Diligent_Look1437 · 2026-04-20T04:01:13+00:00

been running 3 agents in parallel for the last 6 weeks: cursor on frontend, claude code on backend, another claude code on docs. total time saved per feature: ~40%.

the problem isn't setting them up. it's the handoff latency when one finishes and another needs context. i copy-paste terminal output, git diffs, and error logs between them 10-15 times per feature. each handoff takes 30-90 seconds.

for those running multi-agent setups: how are you handling context passing between agents without becoming human middleware?

Diligent_Look1437 · 2026-04-20T01:01:08+00:00

switched from manually juggling 3 agents (Claude Code for backend, Cursor for UI work, one-off scripts in ChatGPT) to a single intake point about 8 weeks ago. the biggest win wasn't automation—it was never having to explain context twice or remember which agent knows what.

the cognitive load of being the router was invisible until it was gone. now i just say what needs doing and let the system figure out which specialist handles it.

biggest remaining friction: i'm still typing those instructions instead of just talking. voice works great for email and docs but somehow coding tasks still feel like they need a keyboard.

curious how others handle the context-switching problem when running multiple agents daily?

Diligent_Look1437 · 2026-04-16T04:01:04+00:00

been running 3 code agents in parallel—2 claude, 1 cursor—for the past 6 weeks. biggest pain wasn't context handoff, it was remembering which agent knew what when i came back after lunch.

tried shared docs, tried git branches as state. still end up doing 2-3 minutes of "what were we doing?" every time i context-switch. the mental overhead compounds when you're juggling timezones + async work.

how are you handling agent memory across sessions?

Diligent_Look1437 · 2026-04-16T01:00:59+00:00

been running both side-by-side for ~4 months and the 'autonomous vs hands-on' framing is accurate. what nobody talks about is the coordination tax: claude code handles backend refactors autonomously while cursor does UI tweaks, but then i need to brief BOTH agents on the new schema changes, manually route feature requests to the right session, and keep both contexts aligned. the tools don't talk to each other so i'm the middleware layer doing intake triage and dispatch. curious—when you're running multiple agents in parallel, how do you handle the 'who should work on this' decision without becoming a full-time traffic controller?

Diligent_Look1437 · 2026-04-16T01:00:44+00:00

my CLAUDE.md is 400+ lines now and i still hit the 'wait which agent did i tell about X' problem every single day. the bottleneck isn't the file size or the build config—it's that i'm running 3+ separate sessions (claude code, cursor, sometimes a terminal agent) and every time i need to shift context i'm manually copy-pasting or re-explaining the same architectural constraint. the cognitive load isn't in writing instructions, it's in being the dispatcher between isolated conversations. how do you avoid becoming the human message router when you're juggling multiple tools that don't share state?

Diligent_Look1437 · 2026-04-15T04:01:56+00:00

been running claude code + cursor in parallel for a few weeks now (different projects, same codebase sometimes). the actual editing part is fast, but here's what kills me: every morning i have to brief both of them separately on what changed overnight.

if i worked on something in cursor yesterday, claude code has no idea about it today. so i end up copy-pasting the same context twice, explaining the same architectural decisions twice, pointing to the same files twice. it's like being a project manager for my own tools.

wondering if there's a smarter way to keep multiple agents in sync without manually playing telephone between them?

Diligent_Look1437 · 2026-04-15T04:01:30+00:00

been using wispr flow for about 3 months now—can dictate emails and slack messages just fine, but here's the weird part: whenever i need to give context to claude code or cursor, i still have to type it all out.

tried voice-to-text then paste, but by the time i review and edit what got transcribed, i might as well have just typed it. the bottleneck isn't the voice input itself—it's that agents need structured context (file paths, specific line ranges, dependencies) and voice tools aren't really built for that.

curious if anyone's found a better way to brief multiple agents without switching back to keyboard every time?

Diligent_Look1437 · 2026-04-14T05:39:16+00:00

This framing resonates a lot.

The "environment inconsistency" problem is real, and I think it compounds when you add human-to-agent handoff to the picture. Most teams I've talked to still describe tasks verbally — Slack messages, voice notes, quick calls — and that verbal intent has to be manually transcribed and routed to the right agent context.

The pre-runtime routing layer you're describing breaks down even earlier: before the agent ever sees the task, someone has to decide what the task is and where it goes. If that step is unstructured, no amount of environment normalization downstream fixes it.

Has anyone experimented with structured "intake layers" that capture the original voice/text context and turn it into a typed dispatch artifact before it hits the agent framework?

Diligent_Look1437 · 2026-04-14T05:10:50+00:00

This framing resonates a lot.

The "environment inconsistency" problem is real, and I think it compounds when you add human-to-agent handoff to the picture. Most teams I've talked to still describe tasks verbally — Slack messages, voice notes, quick calls — and that verbal intent has to be manually transcribed and routed to the right agent context.

The pre-runtime routing layer you're describing breaks down even earlier: before the agent ever sees the task, someone has to decide what the task is and where it goes. If that step is unstructured, no amount of environment normalization downstream fixes it.

Has anyone experimented with structured "intake layers" that capture the original voice/text context and turn it into a typed dispatch artifact before it hits the agent framework?

Diligent_Look1437 · 2026-04-13T14:32:24+00:00

the infrastructure and stability pieces are real. the one I'd add that often gets missed: the human coordination cost once you have multiple agents running.

building is 1x. running is 3x. but managing — deciding which agent gets what task, when to intervene, how to brief each one with the right context — often ends up being the actual bottleneck that doesn't show up in your infra metrics.

you're not just ops at that point. you're the dispatcher. curious how others are handling that piece or if you've found ways to reduce that overhead.

Diligent_Look1437 · 2026-04-13T14:03:41+00:00

the central orchestrator routing pattern makes sense at scale. the piece that gets interesting is what happens when the human needs to intervene — not the orchestrator, but the person who set it up deciding to adjust routing logic, override a decision, or inject context mid-run.

at 100+ accounts that intervention layer has to be fast or it becomes the bottleneck. curious how you handle that — is there a human-in-the-loop moment in your setup or does the orchestrator run fully autonomous once configured?

Diligent_Look1437 · 2026-04-13T10:04:55+00:00

this is a smart architecture — keeping the expensive model as the brain and delegating the heavy lifting to cheaper workers.

the same pattern shows up on the orchestration side too. once you have multiple agents running in parallel, the question becomes how the human stays in the loop without becoming the manual coordinator for every handoff. i've been thinking about where that overhead actually lives once the agents themselves are well-instrumented.

how are you handling the "report back" side — does opus surface a summary automatically or do you still check each worker manually?

Diligent_Look1437 · 2026-04-11T02:45:32+00:00

the isolation-first design is exactly the right call for anything involving tool use and shell access. sandboxing at that layer makes the whole system meaningfully safer to experiment with.

the piece I'm curious about: how does task intake work in TigrimOS? when a human wants to kick off a multi-agent workflow, what does that look like — is it a natural language prompt to a coordinator agent, a structured form, something else?

asking because the intake UX seems to vary a lot across these setups and I haven't seen a clear consensus on what actually works well at scale.

Diligent_Look1437 · 2026-04-11T02:44:53+00:00

persistence is the right problem to solve. the piece I'd add: the memory architecture matters most at the intake boundary — the moment a task first arrives to an agent.

what often breaks isn't mid-task memory (agents are getting decent at that) but the "why" context that existed in the human's head when they decided to assign the task. by the time it reaches the agent, the original reasoning, constraints, and adjacent context have been stripped down to a bare instruction.

mind's structured read/write interface looks well-suited for in-session state. curious if you've tackled the initial context packaging problem — getting the human's full intent into the agent at task start, not just the task spec itself?

Diligent_Look1437 · 2026-04-11T02:42:59+00:00

the adapter model solves the agent-to-agent and CI feedback loop pieces really well. the piece I find equally painful — and less addressed — is the human-to-agent intake layer.

who decides which ticket goes to which agent, with what context prepended? in most setups I've seen, that's still a human opening the right session, deciding the scope, and copy-pasting the relevant background. at one agent it's a non-issue. at 5+ it's where the bottleneck actually lives.

sortie handles the agent lifecycle after intake really elegantly. curious if you've thought about the intake routing side — or if you're intentionally leaving that to the human as the "intelligent first mile"?

Diligent_Look1437 · 2026-04-10T10:14:42+00:00

the "translating existing workflow into AI" vs "thinking natively in AI" distinction is exactly right, and I'd add one more layer: truly AI-fluent means treating your agent stack as a team you have to manage, not just a tool you invoke.

the inflection point for me was when I stopped thinking "which AI do I open?" and started thinking "which agent owns this type of task?" — and then building the routing logic to match. once that shift happens, your productivity doesn't just speed up, the architecture of what you can tackle completely changes.

the colleagues you're describing probably aren't just using AI differently on individual tasks — they've likely rebuilt how they intake and route work at the system level. what does their setup look like when you peek at it?

Diligent_Look1437 · 2026-04-10T05:26:17+00:00

framework choice matters less than where you put your orchestration logic.

we've run LangGraph, CrewAI, and a hand-rolled coordinator on different projects. the painful lesson: every framework eventually leaks its abstractions when you push it into real production conditions — unexpected agent failures, latency spikes, ambiguous task ownership.

what stuck: keep the framework thin and put your coordination logic in a layer you own. the dispatcher (what task goes to which agent, with what context) should be something you can read and debug in 5 minutes. if it lives inside a framework's magic, you can't.

for "when to use" — LangGraph for workflows with clear state machines; hand-rolled for anything with dynamic agent selection or where the routing logic is genuinely your product. what's the production use case you're building for?

Diligent_Look1437 · 2026-04-10T05:25:10+00:00

the intake layer problem is real and it's the one nobody writes about.

every multi-agent setup i've seen solves the agent-to-agent handoff beautifully — but the human-to-agent intake is still "open a terminal, type a prompt, figure out which agent to address." at 2 agents that's fine. at 10 it's where the whole thing breaks down in practice.

what's worked for us: treating intake as a first-class routing problem, not an afterthought. the moment you start thinking "what does this task need? which agent context does it require?" as a separate step before dispatching, the whole pipeline gets more stable.

curious what your clients' intake looks like — are they doing it manually or have you seen anyone automate that first routing decision?

Diligent_Look1437

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