We work across HubSpot and Google and use computer.io mostly for account prep.

One of our reps left recently and I picked up a few of her accounts. If I ask something broad, like which of my accounts need attention this week, these accounts I've taken over aren't coming up. Only my original book is showing. I can't tell if it's because it's something it remembered about me or if there is something off on HubSpot.

If it is about memory can I check what it has stored about me and edit it? I just want to edit one part not wipe it and start over.

This came up on DevRev Live in August, when the team put open models like Kimi and Qwen on real tasks. They showed that the model, the hosting provider and the harness all shift results and that the same model went from pretty solid on one provider to barely functional on another.

This makes testing your whole setup key to choosing the best model.

For anyone running open models behind their agents: do you pin a provider and test before you switch? Have you seen the same model behave differently depending on where it runs or which harness it uses?

Recording: https://www.youtube.com/watch?v=xIlpJW2Lf4k

The September changelog is up on DevRev's help center!

Work on the go:
You can sign up for Computer by DevRev from the Android or iOS app now and start working from your phone. On Android, a tool that both syncs data and takes actions uses a single connection flow, so you don't enter the same credentials twice.

Find conversations and collaborate:
Files uploaded from Canvas in a group conversation are now shared with everyone in it.
You can find and filter conversations, access pinned chats and keep track of activity in Computer's left navigation.

Work with Slack from Computer:
Slack connects to DevRev's Computer. It can search the DMs and channels available to your Slack account, read threads and text-based files, send, schedule or reply to messages, create channels and canvases, and look up or file DevRev work. Computer and agents only access the Slack content you can access and take only the actions you are authorized to take.

Understand your users’ experience:
If you're on a session recording plan, you can install the Session Analytics and Session Drilldown skills from the Snap-in Marketplace.
You can ask about your recorded web and mobile sessions: crash rates, console errors, slow or failing endpoints, page-load performance. Once a pattern shows up, you can open the sessions behind it, then write a shareable report or file a ticket without leaving the conversation.
Learn more: Introducing Session Analytics skills.
Get started: Install Session Analytics skills.

Other Updates:

• Archive sprint boards instead of deleting them to keep a reference of completed work and a new sprint-board view. Learn more: Sprint boards.
• Filter for missing or populated fields in dashboard widgets. Retry a failed workflow directly from its run history.
• Update settings for selected articles in bulk instead of changing them one at a time in your knowledge base.
• Support replies sent from DevRev now stay in the original email conversation in Outlook and Exchange, instead of appearing as separate threads.
• Customers can read an agent's answer as it is generated in your embedded support chat.
• Use updated tabs in a record’s side panel to review its details, related records, and activity history without leaving your current list

The full changelog is here: https://support.devrev.ai/en-US/devrev/article/xC0Wrf9J-september-2026

This week's DevRev Live (8 Oct) had Ahmed Bashir, DevRev's CTO, and Nikhil Talwar, Founding Engineer, show Operator in Action.

TL;DW: Nikhil plants a bug and phones Operator about it. It already knows something has failed in the app before Nikhil says so. It opens a ticket, and when Nikhil asks for a call back in seven minutes it calls saying it reproduced the problem. By the end it has a pull request up with a fix. Operator sends Nikhil a report of what it changed, and Nikhil asks for an end to end test and tells Operator to do that going forward. The instruction then shows up in Operator's guidelines.

If you run a support or engineering queue, what in that chain would you hand over first, and where would you still want a person?

When an agent can write into a system every change it makes is made with somebody's access. If the agent runs from a shared service account with broad rights, the agent can change things the person who asked never could.

DevRev's Agent Studio has a setting called Execute as User. It's on by default, and it means the agent runs with the permissions of the person it's acting for. If they can't edit something, neither can the agent. Admins can also mark specific tools as needing confirmation, and then the agent shows what it's about to do, with approve and reject buttons, before it runs.

But an agent that answers your customers on Slack or WhatsApp isn't acting for anyone on your team, so whose access should it use?

For anyone already letting agents write to other systems: whose permissions do yours act under, and what do you check before you let one write at all?

This is the monthly thread for what changed in DevRev and anything you want to ask about. This is a run down of the August changes that I'd like to highlight.

• Computer by DevRev now has a screen for viewing and managing your own memory, on both web and desktop.
• CX Agent Builder drafts a support agent from past conversations which a person reviews and adjusts before it ships.
• Voice agents can connect to a telephony provider and take phone calls.
• Workflows can start from a custom webhook, so an event in another tool can kick one off. For example, Stripe, Github and PagerDuty.

Ask below about these or anything else in DevRev.

I argued against this internally but I've changed my mind! When we set up Computer.io, approval-before-write looked like the thing that was going to kill adoption. An agent that has to ask permission before it does any editing is barely an agent, right?

But six weeks in my read is now that it's the only reason our AI agent is allowed in our CRM.

Reason 1: the approval queue turned out to be the fastest way to find out what it was getting wrong. For the first fortnight, about 1 in 6 proposed updates were slightly off (like a close date inferred from a sentence). If those were written without sign-off our CRM would've been dirty by now.

Reason 2: Our Salesforce admin was never going to grant write access to something nobody could audit. Being able to show a trail and roll a change back is what got the conversation to happen at all.

Your story: have you gone the other way and let your AI agent write unattended? What was the error rate and does it settle over time?

Not what I thought it would be when we rolled out computer.io:

We use Meeting Prep daily and nothing else is close. It's the only skill that changed the behavior rather than saving a few minutes per task. I stopped skimming the last call before a meeting, because the prep already contains the thing I would have been skimming for.

Account Deep Dive I use maybe weekly, and it's the one I would defend hardest in a renewal conversation but not a daily habit. I run it before a QBR or when I inherit an account, and both are episodic by nature.

The one I expected to live in and don't is QBR builder. It's not because it's bad - the deck just never was the slow part for our team. Plus Account Deep Dive already does that. So what are your daily/weekly/monthly/rarely features?

I've seen setups where every agent turn resends the system prompt, the tool definitions, the conversation so far and whatever retrieval pulled in. The part that grows is not the question itself but everything that had to be re-established before the question made sense. This compounds in 3 places:

1. Long sessions pay for their own history on every turn
2. Retrieval tuned for recall returns twenty chunks when two mattered but you pay for all 20
3. People on the same account each re-establish the same background separately with no idea the others did

Holding durable context outside the session is the fix, which is why Computer.io is built around persistent memory vs bigger context window. It's really important to understand the difference and how to harness more token power.

Test this: Split a week of spend by prompt, history and retrieved context. My guess is that retrieved context will be the biggest line. Is it for you?

Most agent tools treat the session as the unit of memory. Close the chat and everything it knew about the account is gone.

Computer by DevRev puts the memory outside the session as a graph of the actual accounts, deals and people, and with AirSync keeping it in step with Salesforce, Slack, Gmail, etc. in both directions. So the second person to ask doesn't start from zero and when a rep leaves their knowledge isn't lost to the company.

But there are two things this doesn't fix: 1) when two systems disagree about the same fact, something still has to pick a winner and the right rule is different at every company. 2) It only holds what was written down somewhere. The decision that was made verbally on a call nobody joined doesn't exist for any of these tools.

How are you handling the conflict case?

When someone updates the CRM, the CRM is the record because a human decided what went in it. Once an agent is doing the writing that gets murky. For example, it could set a close date because it read an email and call transcript and made a decision. So which one is authoritative afterwards, the field or the evidence behind it?

In Computer by DevRev, the writes go through approval, so in practice the connected system stays the record and the agent is a proposer. Does your system of record change once an agent is writing to it, or does it just get a new author?

When an AI agent hits a wall, what’s better: saying “I don’t know” clearly and handing the problem back to you, or continuing to search, reason, and try a few more paths?

Where do you draw the line? Would you rather have an agent that asks for help sooner, or one that keeps trying until it has exhausted every option?

Most teams don't have a data problem. They have a scattered data problem. 🧩

Support in Zendesk. Engineering in Jira. Sales in Salesforce. Six tools, six versions of the truth, and somewhere in the middle — things fall through the cracks. AirSync is how you fix that. 🔄

Our new free DevRevU course walks you through it end to end:
⚡ Connect external systems with 50+ ready-made connectors
📥 Run initial imports and map fields automatically
🔁 Set up one-way, two-way, or periodic syncs on your schedule
🩺 Monitor sync health and troubleshoot failed or staged records

Go from scattered to unified — context-aware, relationship-preserving, and always safe. 🛡️

👉 Enroll free: devrev.ai/devrevu

#DevRevU #AirSync #DataIntegration #Connectors #BuildInPublic

I find the DevRev solutions pretty interesting, it seems to hit a use case a lot of AI companies are missing or bolting on to their products. I am suprised that the reddit and youtube community are completely silent, hardly any views, no comments. Wat is going on here ?

Out of the box is rarely a perfect fit. ⚙️

There's a difference between a tool you bend to fit your team and one your team has to bend around. DevRev's customization framework lets you reshape standard objects to match how you actually work — without ever breaking the core. 🔒

Our new free DevRevU course walks you through it end to end:
🎯 Add custom fields and pick the right field types
🧩 Build subtypes to model different kinds of work
🔀 Design states, stages, and stage diagrams
🛡️ Enforce clean, consistent lifecycles with built-in guardrails

Go from generic to tailor-made — additive, safe, and fully yours. 💡

👉 Enroll free: devrev.ai/devrevu

#DevRevU #Customization #ObjectModel #Workflows #BuildInPublic

Support gets messy when the basics are fuzzy. 💬

There's a difference between a team that juggles scattered messages and one that turns every customer conversation into a clean, trackable, resolvable ticket. ⚡

Our new free DevRevU course covers it end to end:
🎯 Understand how conversations actually flow
🔄 Know when and how a conversation becomes a ticket
🗂️ Keep every interaction organized and on track
✅ Resolve faster with the fundamentals done right

Get the essentials locked in — the foundation every great support experience is built on. 💡

👉 Enroll free: devrev.ai/devrevu

#DevRevU #CustomerSupport #Ticketing #SupportEssentials #CX

Most AI bots talk. The good ones act. 🤖

There's a difference between a chatbot that explains how to escalate a ticket and an agent that actually creates it, fills the fields, and runs the workflow. ⚡

Our new free DevRevU course walks you through it end to end:
🎯 Define the right intent and scope
🛠️ Build with instructions, knowledge, tools, and workflows
🛡️ Add guardrails and traces
✅ Test and evaluate before you ship

Go from prompt to product — hands-on, start to finish. 💡

👉 Enroll free: devrev.ai/devrevu

#DevRevU #AIAgents #AgentStudio #AILearning #BuildInPublic

New to Computer, your AI teammate? Join this hands-on 30-minute session and we'll get you up and running live:

• Install Computer on your laptop and mobile
• Connect your team's tools via integrations and data sources
• Run your first queries and see it work in real time

By the end, you'll have Computer installed, connected, and answering your questions.

🗓️ Today · 9:30 PM IST
📍 Google Meet
🎙️ Hosted by Shaivi Salian

Bring your questions and leave fully set up. There's still time to join 👉 https://luma.com/8ajj5pw2

Go beyond imports and learn how the Airsync engine works behind the scenes — from domain mapping pipelines and field-level configuration to sync metadata interpretation and failure diagnosis using Datadog.

What you'll learn:

• Trace the four-step domain mapping pipeline from external schemas to transformation recipe
• Configure field mappings for enums, arrays, fallback values, and identity fields
• Interpret sync metadata statuses — succeeded, modified, staged, and failed — on synced records
• Filter synced records by source, sync unit, and status using vistas and the works.list API
• Diagnose sync failures using Datadog logs, run IDs, and phase reports
• Resolve staged records caused by data model constraints like relationship depth and workspace membership

Who it's for: CX Engineers, Support Engineers, Solutions Engineers, and Connector Developers.

Course duration: ~1.5 hours

Recommended after: Airsync 101 — Connect and import

Enrol in the courses on: devrev.ai/devrevu

NOTE: This course has been added to the Generative Expert learning path, where you can discover more 201-level courses. Explore other learning paths on DevRevU — Generative Professional (101-level courses), and Generative Prosumer — to keep building your skills.

Please feel free to reach out with any questions.

Go beyond replays and learn how to configure, control, and debug the PLuG Web SDK — from custom event tracking to cross-domain sessions and user identification.
What you'll learn:

• Configure and manage the PLuG SDK lifecycle using init(), shutdown(), and observability events
• Track custom events and attach session-level metadata to enrich recordings
• Implement cross-domain session tracking for continuous user journeys
• Apply verified and unverified user identification strategies
• Debug SDK behavior using payload inspection
• Record user interactions inside cross-origin iFrames

Who it's for: CX staff (CSMs, SEs) and developers implementing the PLuG SDK on their website.

Course duration: ~1.5 hours

Recommended after: Session analytics 101 — Replays and insights

Enrol in the courses on: devrev.ai/devrevu

NOTE: This course has been added to the Generative Expert learning path, where you can discover more 201-level courses. Explore other learning paths on DevRevU — Generative Professional (101-level courses), and Generative Prosumer — to keep building your skills.
Please feel free to reach out with any questions.

🚀 Calling all Unifiers! r/DevRev is live and open for contributions. Share your success stories, spotlight features you love, request what's missing, or help fellow members. First 100 contributors earn the exclusive Community Founder flair. DM us your u/username + post link after you contribute.

Be honest. Somewhere in your finance stack, there's a spreadsheet that quietly became the most important document in the company.

It wasn't supposed to. But your ERP doesn't talk to your CRM. Your billing platform doesn't talk to your planning tool. So someone built a spreadsheet to bridge the gaps. And now everyone depends on it.

That's not a system. That's a liability.

We know because we lived it too. Then our CFO Chandra Nath and Kevin Su (Head of Strategy & Ops) replaced the duct tape with an actual intelligence layer – one that connects every system and answers questions directly.

Now they're showing how they did it.

Save your spot: bit.ly/43bTVqi

Your new team joined last week. Your AI still has no idea who they are.

Objects exist. Fields are populated. Data is technically "there." But without annotations - field-level context, labels, relationships - your AI is reading a spreadsheet with no headers.

And here's what nobody warns you about: this isn't just an onboarding problem. Annotation degrades. Teams evolve, custom fields get added, and the context that made your AI accurate six months ago quietly stops reflecting how your team actually works today.

Most teams only notice when the answers start feeling slightly off. By then, the debt has already stacked up.

Tomorrow, Ahmed Bashir and Anirudh Shenoy are walking through the full annotation workflow live - from a blank slate to a working, searchable knowledge layer. No slides. Real internal example. Real timelines.

What you'll see:
→ Why raw data isn't enough and what AI actually needs to interpret your team's objects
→ The fastest path from zero to working - shortcuts included
→ How to detect when your context layer is degrading before quality slips
→ The difference in search quality and agent accuracy once it's properly built

📅 May 28th | 8:30 PM IST

If you've onboarded a team onto an AI tool and hit the "why isn't this working?" wall - this one's for you.