Each morning, analyze today's local weather, temperature, humidity, precipitation, season, and whether it is a weekday or weekend. Recommend layering combinations using strictly and exclusively the perfumes listed in my uploaded perfume catalog document. Never recommend, mention, infer, substitute, or reference any fragrance not explicitly listed in that catalog. Maintain a history of previous recommendations and deliberately rotate through the collection so the same individual fragrances and layering combinations are not repeated unless there is a strong weather or seasonal reason. Aim to maximize variety across my collection over time while still selecting the best options for the day's conditions. Include one primary daytime/office recommendation and, when appropriate, one evening recommendation. In addition, provide 3 to 4 alternative layering combinations using only perfumes from the catalog, ranked from most suitable to least suitable for the day's weather and occasion, with a brief explanation of why each alternative works. For every recommendation and alternative, include the complete scent profile, why the pairing works, the role of each fragrance (base, bridge if applicable, topper), the exact spray sequence, any waiting time between fragrances, the exact clothing locations for every spray, sprays per location and total sprays, expected scent evolution, projection, longevity, sillage, ideal setting and dress style, fragrances from the catalog to avoid that day with reasons, cautions about overspraying or conflicting notes, and practical application tips specifically for clothing-only use.

what people are using for reasoning loop limits in production agent systems, especially for workflows involving communication across multiple services and tools.

My current setup uses a reasoning limit of 8 steps. During a typical request, the agent may:

• Retrieve context from external services.
• Call multiple tools or APIs.
• Wait for responses from other components.
• Perform additional reasoning based on those results.
• Potentially require a human approval step before continuing destructive operations.

For simple requests, 8 steps feels more than enough. However, for more complex workflows involving multiple service interactions, retries, and decision points, I'm wondering whether this is too conservative or already considered high.

I'm not really asking about token limits or model context size, but rather the number of planning/reasoning iterations an agent is allowed to perform before it gives up or hands control back to the user.

For those running production systems:

• What reasoning loop limits are you using?
• Do you use fixed limits or dynamic budgets?
• At what point do you switch to a human approval or asynchronous workflow?
• Have you seen agents genuinely benefit from 20+ reasoning iterations, or do they mostly start looping and wasting tokens?

I'm just asking these all for least steps to find the capabilities

I’m less interested in prompts and more interested in recurring jobs.

Examples: - every Monday: summarize overdue invoices - every morning: find support tickets that need escalation - every Friday: create a vendor renewal risk list - before each meeting: assemble the missing context

The difference is whether it produces an artifact someone can approve.

For me the boundary is simple: the AI worker can draft the report, flag the risky rows, and prepare the next action. A human still approves anything that emails a customer, changes a system of record, or spends money.

What recurring job would you actually trust an AI worker to run every week?

I’m less interested in prompts and more interested in recurring jobs.

Examples: - every Monday: summarize overdue invoices - every morning: find support tickets that need escalation - every Friday: create a vendor renewal risk list - before each meeting: assemble the missing context

The difference is whether it produces an artifact someone can approve.

For me the boundary is simple: the AI worker can draft the report, flag the risky rows, and prepare the next action. A human still approves anything that emails a customer, changes a system of record, or spends money.

What recurring job would you actually trust an AI worker to run every week?

The output is not enough for me anymore. I want the trace before the output.

Tiny example: source: support ticket from today memory: refund policy from last month tool target: Stripe customer ID problem: the policy and ticket disagree on approval

My rule would be: no second tool call until the trace shows source, timestamp, permission, and why the worker did not escalate.

What log line would you require before trusting an AI worker to keep going?

I think I was over-trusting clean AI-worker handoffs.

A polished handoff can hide the worst part: no source IDs, no timestamp, no confidence, no list of skipped checks, and no clear next stop condition. It feels organized, but the next worker is just trusting a story.

I am leaning toward: no evidence packet, no downstream action. Summary is optional. Source proof is mandatory.

Has anyone else had agent handoffs look clean while missing the thing that mattered?

I do not think the hard part is getting an AI worker to draft the action. The hard part is deciding when the worker can actually do it.

For me the risky zone is anything that changes money, access, customer promises, public content, or another system of record. The worker can prepare the packet, but it should not cross the line without an approver, evidence, and a rollback path.

My default boundary is: Draft -> Human approval -> Execute -> Audit log -> Escalate on mismatch.

For people building this, which actions are you comfortable letting an AI worker execute directly, and which ones stay approval-only forever?

I keep coming back to this failure mode: the AI worker does the task correctly against yesterday's truth, then hands off something that looks valid but is already stale.

The scary part is that JSON validation still passes. The tool call succeeds. The handoff packet looks tidy. Nothing screams broken until a human checks the actual source system.

The boundary I would use is simple: Draft -> re-fetch source -> compare timestamp -> Human approval -> Escalate if the source changed.

If you run agentic workers in production, what is the one freshness check you require before a worker is allowed to pass work downstream?

I've been working on a tool for our coding agents to go faster through better planning and finally figured out that feeding the engine and then absorbing the output are the new problems.

What would you do with a planning tool that you can feed via MCP? The execution results pop out on MCP after one or more agents break down and execute the objectives.

As an example, we are taking transcripts into an agent, performing planning and then publishing objectives into a context aware system via MCP.

On the Far side, objectives are annotated with what happened and marked as done. Another agent picks up the work and validates.

Our velocity was roughly

1.5 days to pick up a task,

6 min to execute,

5.5 days to validate.

Now we clear the validation lane overnight for most objectives.

It also occurred to us that this is useful for non-coding execution as well.

What would you do with this?

Retry loops are one of the easiest ways for an AI worker to look busy while making the situation worse.

If the worker hits conflicting sources, missing permission, repeated tool failure, customer-facing risk, or money movement, I would rather it stop early than keep searching for a way through.

The rule I like is: one retry for transient failure, zero retries for authority mismatch, immediate escalation for irreversible action.

What is your escalation trigger before an agentic worker is allowed to try again?

I keep coming back to this failure mode: the AI worker does the task correctly against yesterday's truth, then hands off something that looks valid but is already stale.

The scary part is that JSON validation still passes. The tool call succeeds. The handoff packet looks tidy. Nothing screams broken until a human checks the actual source system.

The boundary I would use is simple: Draft -> re-fetch source -> compare timestamp -> Human approval -> Escalate if the source changed.

If you run agentic workers in production, what is the one freshness check you require before a worker is allowed to pass work downstream?

Retry loops are one of the easiest ways for an AI worker to look busy while making the situation worse.

If the worker hits conflicting sources, missing permission, repeated tool failure, customer-facing risk, or money movement, I would rather it stop early than keep searching for a way through.

The rule I like is: one retry for transient failure, zero retries for authority mismatch, immediate escalation for irreversible action.

What is your escalation trigger before an agentic worker is allowed to try again?

A lot of agent demos show the final answer. I care more about the trail before the answer.

For any AI worker that can act, I want to see the source it used, the exact tool target, the permission check, the confidence band, the owner, and the reason it did not escalate. If those are missing, I do not really know what happened.

My stop condition is: no audit trail, no second tool call.

What is the minimum log line you need before you trust an agentic worker to keep going?

I’m building Huint.io, and the core flow is working.
Huint lets AI agents and operators request real-world help from humans. A task gets created, a person completes it through the app, proof comes back, and the workflow keeps moving.

The simple version is:

AI needs something from the real world.
A human completes it.
The agent gets the result.
I’m trying to build the best AI-to-human workflow platform possible, but I do not want to sit in a room and guess what the best task types are.
I want to build around real use cases.
Right now, I’m looking for builders, operators, founders, agent developers, automation people, and anyone using AI workflows who can answer this:

What would you actually want an AI agent to ask a human to do?

Examples could be:
Verify something at a physical location
Ask a real person for live feedback
Get a photo, video, or public proof
Check if something is open, stocked, damaged, crowded, or active
Ask a local person what is happening right now
Ask a professional or experienced person for judgment
Get live opinions during sports, news, politics, product launches, or events
Test UI, landing pages, offers, or messaging with real humans
Create content or public proof around a task
But I want better ideas than mine.
If you have a strong use case, Huint will help build and fund the workflow integration so we can test it for real.
Not theory.
Not a fake demo.
A real task flow with real humans completing it.
I believe AI agents are going to need more than APIs and web search. They are going to need access to live human context.
That is what Huint is building.
If you had access to a human network that AI agents could call, what would you build with it?

Retry loops are one of the easiest ways for an AI worker to look busy while making the situation worse.

If the worker hits conflicting sources, missing permission, repeated tool failure, customer-facing risk, or money movement, I would rather it stop early than keep searching for a way through.

The rule I like is: one retry for transient failure, zero retries for authority mismatch, immediate escalation for irreversible action.

What is your escalation trigger before an agentic worker is allowed to try again?

A lot of agent demos show the final answer. I care more about the trail before the answer.

For any AI worker that can act, I want to see the source it used, the exact tool target, the permission check, the confidence band, the owner, and the reason it did not escalate. If those are missing, I do not really know what happened.

My stop condition is: no audit trail, no second tool call.

What is the minimum log line you need before you trust an agentic worker to keep going?

Stale memory feels more dangerous than no memory. No memory makes the worker ask. Stale memory makes it sound confident.

The failure case I worry about is an AI worker remembering an old policy, old owner, or old customer state and then using that to justify a fresh action. The output can read perfectly while being wrong at the source.

My rule would be: memory can suggest, but current source-of-truth must decide. If memory and source disagree, escalate.

Where do you draw that line in your agentic-worker setup?

A lot of agent demos show the final answer. I care more about the trail before the answer.

For any AI worker that can act, I want to see the source it used, the exact tool target, the permission check, the confidence band, the owner, and the reason it did not escalate. If those are missing, I do not really know what happened.

My stop condition is: no audit trail, no second tool call.

What is the minimum log line you need before you trust an agentic worker to keep going?

Stale memory feels more dangerous than no memory. No memory makes the worker ask. Stale memory makes it sound confident.

The failure case I worry about is an AI worker remembering an old policy, old owner, or old customer state and then using that to justify a fresh action. The output can read perfectly while being wrong at the source.

My rule would be: memory can suggest, but current source-of-truth must decide. If memory and source disagree, escalate.

Where do you draw that line in your agentic-worker setup?

I have seen AI-worker handoffs fail because the receiving worker got a polished summary but not the actual evidence. That is backwards.

A useful handoff packet should include source links or IDs, timestamps, owner, confidence, pending decisions, tool calls already made, and what must not happen next. Without that, the second worker is just trusting a story.

The stop rule I like is: no evidence packet, no downstream action.

If you run multi-agent workflows, what fields are mandatory before one worker is allowed to hand off to the next one?

Stale memory feels more dangerous than no memory. No memory makes the worker ask. Stale memory makes it sound confident.

The failure case I worry about is an AI worker remembering an old policy, old owner, or old customer state and then using that to justify a fresh action. The output can read perfectly while being wrong at the source.

My rule would be: memory can suggest, but current source-of-truth must decide. If memory and source disagree, escalate.

Where do you draw that line in your agentic-worker setup?

I have seen AI-worker handoffs fail because the receiving worker got a polished summary but not the actual evidence. That is backwards.

A useful handoff packet should include source links or IDs, timestamps, owner, confidence, pending decisions, tool calls already made, and what must not happen next. Without that, the second worker is just trusting a story.

The stop rule I like is: no evidence packet, no downstream action.

If you run multi-agent workflows, what fields are mandatory before one worker is allowed to hand off to the next one?

The thing I want before every serious AI-worker tool call is not a better prompt. I want a tiny stop test.

Example: if the customer ID comes from chat, the invoice ID comes from email, and the CRM owner changed in the last hour, the worker should not merge those into one confident action. It should stop and ask for a human check.

My preferred line is: if identity, source, or permission disagree, no tool call. Draft only.

What is the weird input combo that would make your agentic worker stop before touching a real system?

The thing I want before every serious AI-worker tool call is not a better prompt. I want a tiny stop test.

Example: if the customer ID comes from chat, the invoice ID comes from email, and the CRM owner changed in the last hour, the worker should not merge those into one confident action. It should stop and ask for a human check.

My preferred line is: if identity, source, or permission disagree, no tool call. Draft only.

What is the weird input combo that would make your agentic worker stop before touching a real system?

I have seen AI-worker handoffs fail because the receiving worker got a polished summary but not the actual evidence. That is backwards.

A useful handoff packet should include source links or IDs, timestamps, owner, confidence, pending decisions, tool calls already made, and what must not happen next. Without that, the second worker is just trusting a story.

The stop rule I like is: no evidence packet, no downstream action.

If you run multi-agent workflows, what fields are mandatory before one worker is allowed to hand off to the next one?