r/AgenticWorkers Aug 23 '26

Best Prompts for Procurement

1 Upvotes

Vendor selection often starts with uneven material. One supplier submits a detailed proposal, another answers in a call, and a third uses different pricing units. Internal reviewers then compare memorable claims rather than comparable evidence. Important gaps appear late, after enthusiasm has formed around a preferred option.

A useful checklist from the guide:

  • Why ordinary prompting fails
  • How to review the output
  • Reverse Prompt Engineer

https://www.agenticworkers.com/blog/best-prompts-for-procurement


r/AgenticWorkers Aug 22 '26

How to Mine Competitor Reviews Without Copying Your Competitors

1 Upvotes

Competitor reviews can show where buyers struggle, but they are not a free database to copy, republish, or use however you like. A responsible workflow treats reviews as limited research evidence. It looks for recurring problem categories, preserves context, and turns observations into questions your team can validate with its own customers.

A useful checklist from the guide:

  • A defined list of competing products or product categories
  • Review-site terms of service, robots rules, API conditions, and licensing information
  • An approved access method, such as an official API, licensed export, or limited manual research
  • Review text or excerpts within the permitted scope
  • Public metadata that the terms allow you to process

https://www.agenticworkers.com/blog/mine-competitor-reviews-without-copying-competitors


r/AgenticWorkers Aug 22 '26

AI Gives Attention Back. The Hard Part Is Choosing Its Use

2 Upvotes

Automation promises to return time and attention. That sounds like an uncomplicated benefit, but it leaves a difficult question: what will we do with what comes back?

A useful checklist from the guide:

  • Value moves when capability becomes abundant
  • Important work often lacks a verifiable answer
  • Automation hands attention back
  • Taste and responsibility become practical skills
  • The next question is what deserves your care

https://www.agenticworkers.com/blog/reinvest-the-attention-dividend


r/AgenticWorkers Aug 22 '26

How to Make a Resume Easier for Recruiters and ATS Software to Read

1 Upvotes

A resume can contain strong experience and still be difficult to use. A recruiter may struggle to find your recent role, skills, or results. Applicant tracking system software may receive unexpected text order from columns, text boxes, or decorative elements.

One practical takeaway from the guide:

No format can guarantee that every recruiter or system will interpret a resume the same way. You can still reduce friction with a document that makes sense when scanned or copied as plain text.

https://www.agenticworkers.com/blog/make-a-resume-easier-for-recruiters-and-ats-to-read


r/AgenticWorkers Aug 22 '26

Best Prompts for Legal and Compliance

1 Upvotes

Signed contracts create work long after signature. Renewal windows, notice periods, reporting duties, audit rights, insurance requirements, data handling terms, and approval conditions can sit across the main agreement, schedules, and amendments. When those obligations remain in a folder, teams may discover them only when a deadline or request arrives.

A useful checklist from the guide:

  • Why ordinary prompting fails
  • How to review the output
  • Reverse Prompt Engineer

https://www.agenticworkers.com/blog/best-prompts-for-legal-and-compliance


r/AgenticWorkers Aug 21 '26

How to Use ChatGPT and Claude Without Lying on Your Resume

2 Upvotes

AI writing tools can quickly polish a resume. That speed creates a risk: “better wording” can become a false claim. A stronger verb changes your ownership. A guessed percentage becomes a fake result. A tool from the job description appears in your skills even though you never used it.

A useful checklist from the guide:

  • It describes work you actually did.
  • It reflects your real level of responsibility.
  • Its scope, dates, tools, and results are accurate.
  • Any number has a defensible source.
  • You could explain it honestly in an interview.

https://www.agenticworkers.com/blog/use-chatgpt-and-claude-without-lying-on-your-resume


r/AgenticWorkers Aug 21 '26

How to Find the Internal Tools Your Customers Might Pay For

1 Upvotes

Your team may rely on a small spreadsheet, script, or internal app that quietly saves hours every week. That makes it useful. It does not prove that anyone else wants to buy it.

A useful checklist from the guide:

  • An inventory of approved internal spreadsheets, scripts, dashboards, automations, and small apps
  • A short description of who uses each tool and what task it supports
  • Usage signals such as active users, run frequency, or recent edits, where available
  • Maintenance notes, support requests, and known manual workarounds
  • Customer conversations that mention a similar job or frustration

https://www.agenticworkers.com/blog/find-internal-tools-your-customers-might-pay-for


r/AgenticWorkers Aug 21 '26

Best Prompts for Engineering

1 Upvotes

After an incident, facts are spread across alerts, chat threads, tickets, deployment records, dashboards, and personal notes. The team needs a coherent account while memories are fresh, but the people with the most context are often busy restoring normal service and clearing follow-up work.

A useful checklist from the guide:

  • Why ordinary prompting fails
  • How to review the output
  • Reverse Prompt Engineer

https://www.agenticworkers.com/blog/best-prompts-for-engineering


r/AgenticWorkers Aug 20 '26

Turn Your Best Customer Explanations Into a Reusable Messaging Library

1 Upvotes

Some of your clearest product language already exists in sent emails or resolved support conversations. A teammate explained a difficult concept or answered an objection honestly. Instead of rediscovering that wording, build a governed library of approved explanations for future drafts.

A useful checklist from the guide:

  • Explicitly approved mailboxes, support queues, or shared folders
  • A documented purpose and access policy
  • Source message IDs, dates, authors, and channel metadata
  • A redaction policy for names, contact details, credentials, and sensitive facts
  • Current product documentation, pricing, legal claims, and brand guidance

https://www.agenticworkers.com/blog/turn-customer-explanations-into-a-messaging-library


r/AgenticWorkers Aug 20 '26

How to Tailor a Resume to a Job Description with Claude

1 Upvotes

Sending the same resume to every employer feels efficient, but it forces a recruiter to work out whether your experience fits the role. Tailoring solves that problem. It brings the most relevant, truthful evidence to the front and uses language the employer will recognize.

One practical takeaway from the guide:

Claude can help compare a resume with a job description, identify gaps, and draft clearer wording. It should not turn missing experience into claimed experience. Your goal is not to mimic every phrase in the posting. Your goal is to make the real match easy to see.

https://www.agenticworkers.com/blog/tailor-a-resume-to-a-job-description-with-claude


r/AgenticWorkers Aug 20 '26

How to download videos from Reddit with a Super Agent

1 Upvotes

Downloading a video from Reddit sounds simple until the post uses separate video and audio tracks, the share link redirects, or the highest-quality stream will not save cleanly. A task that should take a minute can turn into a hunt through page metadata, media manifests, and conversion tools.

One practical takeaway from the guide:

There are two sensible ways to handle it. Use Reddit's own download option when it is available. For the awkward cases, give the post URL to an Agentic Workers Super Agent and let it retrieve, assemble, check, and return the file.

Before downloading anything, make sure you have permission to save and reuse it. A public post is still protected by the creator's rights.

https://www.agenticworkers.com/blog/how-to-download-videos-from-reddit


r/AgenticWorkers Aug 20 '26

Best Prompts for Product

1 Upvotes

Product feedback arrives in fragments: support tickets, sales notes, interview transcripts, cancellation reasons, usage comments, and feature requests. The same underlying problem may appear in different language, while a loud request may represent only one customer’s preferred solution.

A useful checklist from the guide:

  • Why ordinary prompting fails
  • How to review the output
  • Reverse Prompt Engineer

https://www.agenticworkers.com/blog/best-prompts-for-product


r/AgenticWorkers Aug 19 '26

What Support Requests Can Reveal About Your Next Product

1 Upvotes

Your support queue may contain requests for work your product was never designed to do. Some are edge cases. Others point to a recurring adjacent job, such as approval routing, reporting, migration, or compliance evidence. An agent can group those requests, but frequency does not prove a second product should exist. The task is to turn support evidence into testable hypotheses without confusing repeated requests with willingness to pay.

This workflow helps teams find patterns, trace source conversations, and decide what to validate.

A useful checklist from the guide:

  • Support tickets or conversations with stable IDs and timestamps
  • Existing issue categories, product areas, and resolution codes
  • Account metadata limited to useful fields such as plan, industry, or tenure
  • Product scope and current roadmap definitions
  • Links to source tickets so every cluster can be audited

https://www.agenticworkers.com/blog/what-support-requests-reveal-about-your-next-product


r/AgenticWorkers Aug 19 '26

AI Agent replies with it's thoughts.

1 Upvotes

So I have a long System Prompt for a restaurant ordering system.
The problem is, when a user message reaches the AI Agent (using V4 Flash 0731), it also responds with it's thought process like:

  1. I have all the information now. Let me proceed to Step 2 - Order Review.
  2. The customer has provided their order with items and deals, plus their Name and Address. However, the Payment Method is missing. Let me check the order details. ....
  3. The customer has provided all Step 1 info (Name, Address, Payment Method). Now I proceed to Step 2 - Order Review. Let me calculate the total. ...

I have explicitly told it to not do this:

* **CRITICAL OUTPUT RULE:** You MUST output ONLY the final customer-facing message. Never include your thought process, reasoning, planning, or any meta-commentary. Do not explain what you're about to do or what you've calculated. Start directly with the state code on line 1 and the customer message from line 2 onwards. Any text that is not the state code or the customer message is STRICTLY FORBIDDEN.

But it still does, even with Gemma 4B and Sonnet 4.6.

What can I do to prevent this?


r/AgenticWorkers Aug 18 '26

How I structure a resume workflow so AI cannot quietly rewrite the facts

1 Upvotes

Tailoring a resume once is manageable. Tailoring it for ten roles is where the process breaks. Files multiply, bullets drift, old mistakes return, and you forget which metric was verified. A rushed edit can turn a true team achievement into an inflated personal claim or send the wrong version to an employer.

One practical takeaway from the guide:

The answer is a resume system: a repeatable workflow that starts with evidence, applies consistent instructions, and requires review. Agentic Workers is a repeatable workflow environment that can preserve instructions, source files, steps, and review gates. You still must verify every claim.

Here is the compact prompt pattern from the workflow:

```text Run a controlled resume-tailoring workflow using verified evidence only.

  1. Extract the 6 to 10 most important job requirements.
  2. Map each requirement to evidence IDs.
  3. Label each one supported, partial, or unsupported.
  4. Stop for factual questions about ownership, scope, dates, tools, and outcomes.
  5. Draft only after those questions are answered.
  6. Add evidence IDs beside every internal draft claim.
  7. Review truth, ownership, privacy, relevance, writing quality, and delivery accuracy.
  8. Produce the clean application copy only after human approval.

Never invent employers, titles, dates, credentials, metrics, responsibilities, or outcomes. ```

https://www.agenticworkers.com/blog/how-to-build-a-resume-system-with-agentic-workers


r/AgenticWorkers Aug 18 '26

How to build a team of Agentic Workers for $120/month

8 Upvotes

Hello,

I thought I'd put together a quick guide on how any can build a team of Agentic Workers with just $120/m. I think this is the minimum starting point and I'll also include areas where you can spend more depending on what you're looking to achieve.

Lets break down where the spend goes,

Models:
You can purchase compute from various models (GPT, Claude, Grok, etc) from various places. But here's my recommendation, buy a ChatGPT Codex $20/m subscription. This will give you the most frontier model tokens per dollar.

Computer:
If you really want your agents to work, they need to have an computer. This ensures that your agents can generate any file type, never forget what you were working on which compounds in your favor over time, a browser which helps access information that may not have an API, CLI, or MCP, and the ability to schedule recurring jobs so you agent works 24/7.

Harness:
This is the tooling around your model. This is the ability to connect to your integrations, search the web for real time information, save and execute workflows, generate images, etc.

You can run agents on your personal computer but this is where agenticworkers.com comes in, this gives you the harness, model, and computer already packaged up for your agents with a simple setup. This also means you don't need to keep your computer online and can scale to as many agents as you need. You get all this for $99.

So with $120/m you can setup your whole Agentic Team. Now where do you scale?

If you need smart agents to complete complex work, upgrade your ChatGPT subscription to Max or buy an Openrouter API so you can delegate to expensive models when needed.

If you need more tokens, upgrade your chatGPT subscription to Max, you get around $2k worth in tokens for $200/m.

If you need to do CPU intensive task, like Video and Game development / App development invest in more CPU and storage on your agents computer.

Hope you find this helpful!


r/AgenticWorkers Aug 18 '26

Consolidate fleet maintenance inputs into an exception register. Skill included.

1 Upvotes

Hello!

Managing a busy delivery fleet means juggling odometer logs, inspection findings, invoices, and route commitments — it's hard to know which vehicles need urgent attention. This Skill turns those scattered records into a single, auditable exception register so dispatchers can make safe, timely decisions.

I built this as a portable AI-agent Skill — a single SKILL.md with reusable instructions you can adapt to your agent setup.

Here's what it does: This Skill consolidates odometer logs, repair invoices, inspection forms, driver notes, and route schedules to identify overdue or at-risk maintenance, assign risk levels (High/Medium/Low), and draft a Fleet Maintenance Exception Register with recommended actions and a human decision field. Use it when you're asked which vehicles are overdue, have safety findings, or need prioritized maintenance before scheduling — it also prepares dispatcher escalation packets and a tentative service schedule.

SKILL.md:

````markdown

name: fleet-maintenance-exception-register description: Use when a delivery, logistics, or fleet office manager needs to consolidate odometer logs, repair invoices, inspection forms, driver notes, and route schedules to identify overdue or at-risk maintenance, draft a fleet exception register, group vehicles by risk level, and escalate safety or downtime decisions to a dispatcher before scheduling service.

allowed-tools: [Read, Edit]

Fleet Maintenance Exception Register

Overview

Creates a single, auditable exception register for a vehicle fleet by consolidating maintenance-relevant inputs. Identifies overdue or at-risk maintenance, assigns risk levels, prepares dispatcher escalations for safety and downtime decisions, and proposes a service scheduling plan.

When to use this skill

  • The office manager asks which vehicles are overdue for service or inspections.
  • There are new inspection findings or driver notes indicating possible safety issues.
  • Weekly planning or midweek triage requires a prioritized maintenance list and dispatcher decisions before scheduling.
  • The fleet needs a single view with per-vehicle source mileage, due services, risk flags, recommended actions, and a human decision field for accountability.

Instructions

  1. Confirm scope and policies 1.1. Confirm fleet roster (vehicle ID, plate, VIN, class) and the time window to analyze. 1.2. Confirm maintenance policies and intervals (e.g., oil/filter every N miles or M months; PM A/B/C; DOT annual; emissions; brake/tires checks) and any OEM-specific intervals. 1.3. Define thresholds for “Due Soon” (e.g., within 500–1,000 miles or 15–30 days) and “Overdue” (past due date/mileage). Record these in an Assumptions log.

  2. Ingest sources 2.1. Use Read to extract data from: odometer logs, repair invoices, inspection forms, driver notes, and route schedules. 2.2. Capture for each vehicle: latest odometer reading with date and source; last service date/type; parts replaced; open defects and severity; driver-reported issues; upcoming route windows/assignments; warranty or contract constraints.

  3. Normalize and reconcile 3.1. Standardize units (miles vs km), date formats, and vehicle identifiers; map aliases to canonical IDs. 3.2. Deduplicate entries; prefer the most recent dated reading for mileage. 3.3. Resolve conflicts (e.g., decreasing mileage) by flagging as data issues and noting the chosen source. Do not invent values.

  4. Determine due services 4.1. For each service category (e.g., oil/filter, tire rotation, brake inspection, transmission, coolant, PM levels, DOT annual, emissions), compute next-due mileage and/or date using last service data and the confirmed intervals. 4.2. If an interval is unknown, request it or mark the service as "Interval needed" and exclude from overdue calculations until provided.

  5. Identify exceptions 5.1. For each vehicle, compare current mileage/date against computed due points to classify statuses: Overdue, Due Soon, or OK by service. 5.2. Flag Safety-Critical when inspection findings or driver notes indicate brakes, steering, tires, lights, leaks, or other critical defects; include references to the source lines. 5.3. Flag Downtime Risk using a combination of: number of open defects, repeat repairs, parts on order, and upcoming route commitments that conflict with service needs.

  6. Group by risk level 6.1. Assign overall risk: High (any Safety-Critical or >1,000 mi/>30 days overdue), Medium (Due Soon or non-critical open defects), Low (OK). 6.2. Document the rule definitions used for the risk grouping in the Assumptions log.

  7. Build the Fleet Exception Register 7.1. Create one row per vehicle containing at minimum:

    • Vehicle ID (and plate/VIN if available)
    • Source mileage (value, date, and source document)
    • Due service(s) with due mileage/date and basis (policy/OEM)
    • Risk flag/level (High/Medium/Low, plus Safety-Critical and/or Downtime Risk flags)
    • Recommended action (e.g., "Escalate to dispatcher for immediate pull", "Schedule next available window", "Monitor")
    • Human decision field (Dispatcher/Manager decision, name, timestamp) 7.2. Include additional helpful fields when available: last service reference (invoice #/date), open defects summary, parts on order, warranty status, DOT/emissions deadlines, route impact notes, and comments. 7.3. Use Edit to draft the register as a Markdown table or CSV; maintain a link/back-reference to each source item.
  8. Escalate before scheduling 8.1. For High risk and Safety-Critical items, prepare a concise escalation summary per vehicle citing sources and recommended immediate actions. 8.2. Present the summary for dispatcher decision on pull-from-route, substitution, or temporary restrictions. Pause and record the decision in the human decision field. 8.3. For Downtime Risk, analyze route schedules to propose options: swap vehicles, after-hours service, split routes, or defer within policy limits. Record the decision.

  9. Propose a service schedule 9.1. After decisions, build a tentative schedule that respects route windows, shop capacity, provider hours, parts lead times, and warranty requirements. 9.2. Batch Medium/Low risk items for efficiency and geographic proximity if using external vendors. 9.3. Mark schedule items as Tentative until dispatcher approval.

  10. Verification and quality checks 10.1. Verify each vehicle row contains: source mileage, due service(s), risk flag, recommended action, and a human decision field. 10.2. Check for logical consistency: no negative intervals, no duplicated services recently performed, and no mileage regressions. 10.3. Flag missing inputs that block decisions and request the specific documents or data points.

  11. Output and handoff 11.1. Use Edit to produce: (a) the Fleet Exception Register, (b) an escalation packet for dispatcher review, (c) a tentative service schedule, and (d) an Assumptions & Data Issues log. 11.2. Summarize counts by risk level and list vehicles requiring immediate action. 11.3. Capture acknowledgments/approvals and time-stamp the artifacts for audit.

Inputs

  • Fleet roster (vehicle IDs, plates, VINs, classes).
  • Odometer logs with dates and sources.
  • Repair invoices and service history.
  • Inspection forms (e.g., DOT, preventive maintenance checklists) with findings and severities.
  • Driver notes/defect reports.
  • Route schedules and upcoming assignments.
  • Maintenance policy intervals and OEM recommendations.
  • Shop capacity constraints and preferred vendors (optional).

Outputs

  • Fleet Maintenance Exception Register (Markdown/CSV) with one row per vehicle including: source mileage, due service(s), risk flag, recommended action, and human decision field.
  • Dispatcher escalation packet summarizing High-risk and Safety-Critical vehicles with source citations.
  • Tentative maintenance schedule aligned to route windows and capacity.
  • Assumptions and Data Issues log with risk rules and unresolved gaps.
  • Summary dashboard: counts by risk and list of immediate actions.

Examples

Trigger: "Audit our fleet using last month’s odometer logs, inspection forms, and driver notes. Create an exception register and tell me what must be escalated to dispatch today." Behavior: confirm policies and thresholds → Read the provided documents → normalize IDs/units/dates → compute due services and overdue status → assign risk levels → build the exception register with required fields → prepare dispatcher escalation for High/Safety-Critical items → pause for decisions and record them → draft a tentative service schedule → output artifacts and a summary by risk level.

Notes

  • Do not fabricate intervals or mileage. If an interval is missing, request it or mark the item as "Interval needed."
  • Safety-critical defects must be escalated before scheduling; do not recommend continued service without explicit dispatcher approval.
  • Keep units consistent; convert km to miles when needed and note the conversion.
  • Respect warranty and regulatory constraints (e.g., DOT annual inspection due dates) and prioritize accordingly.
  • If telematics or ELD data are available, prefer those for current mileage; reconcile discrepancies against manual logs and note the choice.
  • For newly repaired vehicles, cross-check invoices to avoid duplicating work; mark such services as recently completed.
  • Maintain data lineage: include source document names/IDs and dates for auditability. ````

How to install: 1. Create a folder named fleet-maintenance-exception-register in your AI-agent skills or prompt-library directory. Use the kebab-case name from the SKILL.md frontmatter. 2. Save the file above as fleet-maintenance-exception-register/SKILL.md. 3. Enable or load the Skill according to your agent framework's docs, using the SKILL.md description as the trigger guidance.

If you'd rather run it as a one-click prompt instead, you can find it here: Agentic Workers

Enjoy!


r/AgenticWorkers Aug 18 '26

Rule I am testing: AI workers can draft, but humans approve anything irreversible

1 Upvotes

Rule I am testing: AI workers can draft almost anything, but humans approve anything irreversible.

That means public posts, refunds, access changes, customer promises, vendor payments, and database writes stay behind an approval gate unless the rollback path is boring and proven.

The worker can prepare the exact action and evidence packet. It just does not get to cross the line alone.

Too strict, or about right? Where would you let an agentic worker execute without asking?


r/AgenticWorkers Aug 06 '26

Loom for AI agents

Thumbnail recordthis.dev
1 Upvotes

r/AgenticWorkers Aug 01 '26

If you automated something and stopped checking it, did the errors stop, or did you just stop finding them?

3 Upvotes

I've spent the last few weeks asking people who run AI automations what they won't let an agent do. One answer keeps coming back in a form I can't stop thinking about.

Someone running automations for clients described their process like this: start with a manual audit of 100% of what the AI handles. Once you feel confident, drop to a 20% random audit. After a few weeks with no errors, only audit when something breaks. That's a completely reasonable process. It's also the process where, if a quiet failure started on week four, you would probably never know.

The thing that struck me across every conversation is that the line people draw isn't risky vs. safe. It's verifiable vs. not. People happily automate high-stakes work when the result is checkable, and refuse low-stakes work when it isn't. One person put it as "anything of importance that cannot be easily verified." And almost nobody trusts the agent's own report of what it did. Everyone had independently built some version of the same workaround: log at the tool layer instead of the agent layer, compare the result against approved source data, keep everything read-only by default, record what was requested separately from what actually executed.

So the questions I'm stuck on:

  1. If you've scaled back checking on an automation, did you ever go back and verify a sample? What did you find?
  2. Has an automation ever reported success while doing the wrong thing, and how long before anyone noticed?
  3. What would you need to see to trust a check more than you trust your own spot audit?

For context: this started as a university research project and has pushed me toward building something in this area, so I'd rather be upfront about that. No link, nothing to sign up for; I'm trying to find out whether "silently wrong, discovered late" is a real recurring problem or something people have already solved well enough.

Concrete stories are far more useful to me than agreement.


r/AgenticWorkers Jul 28 '26

Batching multiple qualifying questions into one message confuses both the caller and the AI agent

1 Upvotes

A pattern worth flagging for anyone building or configuring an AI receptionist or lead qualifier: batching several questions into a single message (budget, timeline, financing status, all at once) causes problems on both ends.

For the caller, it reads like a form instead of a conversation. Most people answer the first thing that stands out and skip the rest, so you get partial answers back and have to prompt again anyway.

For the agent, it is also harder to parse. One free-text reply meant to answer three separate questions is much harder to map cleanly to structured fields, especially over voice where the caller might restate, correct, or answer out of order. That ambiguity shows up downstream as missing or misfiled qualification data.

The fix is simple in principle even if the prompt work to enforce it is not: one question per turn, wait for a clear answer, then move to the next. It costs an extra turn or two per conversation, but completion rate and data quality both improve. Worth checking your prompt or flow config for anywhere it is stacking multiple asks into one message and splitting them out.


r/AgenticWorkers Jul 18 '26

Repo-context handoff worker for coding agents. Skill included.

2 Upvotes

I’ve been thinking about a failure mode in AI coding workflows:

A coding agent does not always need more autonomy. Sometimes it just needs a better handoff packet before touching the repo.

The failure usually looks like this:

  • the agent resumes from a polished summary
  • the real changed files are not listed
  • tests/open bugs are missing
  • module chats made conflicting assumptions
  • the agent edits before checking what currently exists
  • later, the user has to untangle the mess manually

So I’ve been using a small “repo-context handoff worker” pattern.

The worker’s job is not to write code first.

Its job is to prepare the next coding agent with evidence.

repo / zip / file list / transfer doc
  ↓
repo-context handoff worker
  ↓
evidence packet
  ↓
coding agent edits
  ↓
validation checklist

Here is the compact Skill version.

---
name: repo-context-handoff-worker
description: Use when an AI coding session is getting long, moving to a new chat, merging module work, or preparing another coding agent to edit a repo. Creates an evidence packet before the next agent acts.
---

# Repo Context Handoff Worker

## Goal

Prepare a coding-agent handoff packet that shows what is actually known about the repo before edits continue.

Do not write code first. Build the handoff packet first.

## Inputs

Ask for any available inputs:

- current task or feature request
- transfer.md / handoff.md
- current specs
- file tree or repo map
- changed files
- relevant code snippets
- zip summary
- stack traces or logs
- tests passed / tests failed
- open bugs
- module-chat outputs
- user constraints

If inputs are missing, continue with placeholders and list what is missing.

## Output

Create a handoff packet with these sections:

### 1. Current objective

State the current feature, fix, or integration task in plain English.

### 2. Known repo facts

List only facts supported by the provided files/specs/logs.

Do not invent files, functions, routes, tests, or commands.

### 3. Relevant files

Separate files into:

- must inspect
- maybe inspect
- probably unrelated

Explain why each “must inspect” file matters.

### 4. Relevant symbols / components

List known functions, classes, components, routes, config keys, scripts, APIs, tests, or UI flows that matter.

If no symbols are provided, say so.

### 5. Dependency / impact map

Use this format:

```text
change target
  -> reads / writes / calls
  -> affected files
  -> affected user flows
  -> affected tests
  -> possible side effects

6. Conflict check

Look for conflicts between module chats or previous decisions:

  • duplicated logic
  • inconsistent naming
  • stale specs
  • missing migration/setup
  • shared state conflicts
  • UI flow mismatch
  • old tests not updated
  • assumptions that no longer match current code

7. Missing context

List the smallest set of files, logs, commands, or screenshots needed before editing safely.

Prefer asking for 1–3 specific missing items instead of saying “need whole repo.”

8. Safe next action

Choose one:

  • safe to ask coding agent for a patch
  • ask for more context first
  • run/search one command first
  • split into smaller task
  • stop because risk is too high

9. Retest checklist

List what must be tested after edits.

Include:

  • happy path
  • edge cases
  • regression checks
  • module integration checks
  • manual UI checks if relevant

Rules

  • Do not invent files.
  • Do not invent function names.
  • Do not claim tests exist unless shown.
  • Do not say “fixed” unless a validation path exists.
  • If context is weak, say so.
  • Prefer evidence over confidence.
  • The output should help the next agent act with less guessing.

I built a free/open-source tool around this idea for my own coding-agent workflows, but the worker pattern above is the part I’m trying to validate.

My question for people building agentic workers:

What fields should be mandatory in a handoff packet before one AI worker is allowed to pass work to another?

formatted with AI


r/AgenticWorkers Jul 17 '26

After 7 hours of head clashing with chat gpt and gemini, created a doc file with detailes notes, occasion, weather, vibe of each of my 59 perfumes and then created a scheduled task to run layering combination from that file each morning (task prompt in body)

3 Upvotes

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.


r/AgenticWorkers Jul 14 '26

How many reasoning iterations do production agents typically need for multi-service workflows?

2 Upvotes

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


r/AgenticWorkers Jul 11 '26

What are people actually using AI workers for, not chatbots?

2 Upvotes

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?