Zencoder zaps zero-sum zonking with Zenflow Work

Zenflow Work

Zenflow Work is an expansion of its AI orchestration platform designed to accelerate “everything around the code” today, so that means the planning, the coordination, the reporting and the communication that consume most of an engineer’s day. 

The technology also brings the same structured, goal-driven AI workflows to product, marketing, sales, customer success, finance and HR teams.

As we know, AI coding agents have changed how software gets built. Engineers use Claude Code, Codex, Cursor (and others) to write, test and review. 

Zapping zero-sum zonking 

But here’s the reality i..e ask any engineer how they actually spend their day, and coding is maybe a quarter of it – the rest is chasing information, sitting in meetings, reviews, emails, documents and prep work. This is the “zonk-out factor” (zonked out, being laid flat out with fatigue or exasperation) that means developers spend time on zero-sum activities that don’t really add productive value to a project the same way that functional code does.

“Coding agents made engineers 10x faster at writing code. But there’s actually more routine work outside of coding than there is in coding, with people spending three quarters of their time doing that,” said Andrew Filev, CEO and founder of Zencoder. 

Zenflow Work introduces goal-driven automations that handle the work surrounding code: multi-step workflows that execute across Jira, Linear, Notion, Gmail, Google Docs (and more), which conclude automatically once success criteria are met.

There are standup briefs i.e. every morning, the agent searches Jira for issues updated in the last 24 hours, groups them by what shipped, what’s in progress, and what’s blocked, and writes a 5-bullet summary. The agent reads pull requests (PRs) merged to main during the week, categorises them by feature, fix and improvement, then writes user-facing release notes in Google Docs. 

“What used to take half a day takes 10 minutes of review,” asserts Filev and team.

The agent compiles completed issues from Jira, drafts a weekly update email in Gmail, and saves it as a draft for review. The agent checks Google Calendar for tomorrow’s product review, finds all Jira and Linear issues mentioned in the invite, pulls their status, reads the Notion spec and writes a prep doc. This means that 45 minutes of tab-switching becomes a single task.

Zenflow’s model-agnostic architecture is also an economic advantage. Internal research by the company suggests that routing each workflow stage to the right model – rather than running a single frontier model end-to-end – reduces LLM costs by 70% while maintaining or improving output quality. Using Opus end-to-end costs more than 2x what Zenflow’s orchestrated approach costs, pairing Opus for planning with Gemini Flash for implementation as an example. The orchestration layer manages this routing automatically.

Not just for developers

Sales reps spend more time on proposals and follow-ups than selling. Marketers spend more time coordinating than creating. Finance teams spend more time chasing receipts than analysing spend. Zenflow Work handles the repetitive multi-step work across all of them.

Users can start tasks, get real-time updates, and chat with their Zenflow agent directly from Telegram or Slack, with WhatsApp support coming later. The same messaging-first interaction that made OpenClaw a phenomenon, delivered through structured workflows and a curated set of integrations with a tighter security perimeter.

“Zenflow’s orchestration approach is already proven on the coding side: in internal benchmarks, intelligent routing across pipeline stages made coding 2.7x cheaper per resolved task, at equal or better quality. Zenflow Work brings that same multi-model engine to business workflows,” said the company, in a press statement.

Business app integrations

Zenflow agents connect directly to the tools teams use daily, reading, writing, and coordinating across platforms. 

• Project Management: Jira, Linear, Notion
• Communication: Gmail, Slack, Telegram
• Productivity: Google Docs, Slides, Sheets, Drive, Google Calendar

CWDN: Your 70% LLM cost reduction claim is striking — which specific workflow stages benefit most from model-routing decisions?

Filev: The biggest savings come from separating planning from execution. Planning and reasoning, figuring out what to do, breaking a task into steps, deciding which tools to call, that’s where you need a frontier model like Claude Opus. But once the plan is set, the actual implementation steps, reading from Jira, formatting a summary, writing to a Google Doc, don’t need the most expensive model on the planet.

A model like Gemini Flash handles those perfectly well at a fraction of the cost. The orchestration layer handles this routing automatically, so the user doesn’t think about it. They just get the same quality output for significantly less.

Andrew Filev, CEO & founder of Zencoder.

CWDN: With 75% of engineer time spent outside coding, how does Zenflow prioritise which non-coding tasks to automate first?

Filev: Coding agents solved the hardest part of the problem, but they also revealed the bottleneck that was hiding behind it. When you can write code 10x faster, suddenly the standup prep, the stakeholder emails, the meeting prep, the release notes become the constraint.

We started with workflows that can run on autopilot, on a schedule, with zero human input needed to trigger them. Your standup brief assembles itself from Jira every morning before you sit down. Release notes compile from merged PRs every Friday. Stakeholder updates draft themselves weekly from real project data. Think of it like giving every engineer and PM an executive assistant who already did the prep work before you walked in.

CWDN: How does Zenflow Work ensure AI-generated standup briefs and release notes maintain accuracy without constant developer oversight?

Filev:  We think about this the same way you’d think about introducing any new process in an organisation. You start in dual mode: the AI runs, a human reviews, and you use that period to both calibrate the system and establish what “good enough” looks like for that specific workflow. Once you have high confidence in the output, you graduate to what we call a double-LLM pattern, where a second model checks the first for consistency and only escalates to a human when something looks off. 

Our orchestration allows you to implement this pattern with a simple configuration change. For the workflows we’re talking about here, standup briefs, release notes, stakeholder updates, the risk profile is fundamentally different from, say, medical decisions or financial transactions. These are internal artefacts consumed by teammates who have their own context and can catch errors naturally. That’s an additional layer of quality control built into the environment itself. So you get three layers: structured source data going in, automated consistency checks in the middle, and informed human consumers at the end.