Many AIs, One Workflow

Many AIs, One Workflow.

Different AIs for different steps. Same workflow. Same memory. One AI researches. Another drafts. A third reviews. Every step on the same task. Every note in the same place.

Start with ConvOps See how it works

One workflow · a team of AIs

1Research Claude

2Draft ChatGPT

3Compliance review Audit agent

Shared notesevery AI reads the same stream

1
Workflow across every AI on the task: 1
Shared notes stream per task: 1
Audit trail across every AI

A team of AIs

A team of AIs, each driving their step, coordinated by one workflow.

Nobody uses one AI for everything. The best teams use Claude for reasoning, Cursor for code, ChatGPT for drafts, custom agents for domain work. Without shared process and shared memory, that is fragmentation — three silos, zero hand-off. ConvOps organizes every AI inside a shared workflow. The AIs are the actors. The workflow is the backbone. Coordination, not chaos.

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Five AIs. One task. One workflow. Each AI drives the step it is best at.

Per-step AI assignment

Each step declares which AI drives it.

Multi-agent without the framework tax. Each step in a workflow can specify which AI should drive it — research step to Claude, drafting step to ChatGPT, review step to a custom compliance agent. When the workflow advances to that step, that AI takes the wheel. No orchestration code. No framework to learn. Multi-agent as a config field.

In practice

Research step to one AI. Drafting step to another. Review step to a third.
The workflow advances — the assigned AI picks up where the previous one left off.
Change the assignment — the next run uses the new AI. No migration.

Shared notes across AIs

Every AI reads the same notes. Every AI writes to the same stream.

Every task has a shared notes stream. Any AI working on the task can post a note — a decision, a progress update, a blocker, context, an observation. Every AI reads the same stream. When work hands off from one AI to another, the next AI sees the full history — not a summary, not a hand-off doc, the actual notes the previous AI wrote as it worked.

Shared notes · Q2 blog post

One task · Four AIs

Claude posted a note on Research step

Step 1 · 09:14

ChatGPT advanced the workflow at Draft step

Step 2 · 09:41

Compliance agent requested human approval on Review step

Step 3 · 10:07

Copilot resumed after approval on Publish step

Step 4 · 10:22

Hand-off without re-briefing

The next AI picks up where the last AI left off.

Hand-off between AIs is where most multi-agent setups break. Copy-paste and pray. ConvOps hands off through the workflow itself — the state, the step, the notes, the audit entries all travel with the task. The next AI opens to the current step with everything the previous AI did already documented. No re-explaining. No re-briefing. No prompt doctoring.

In practice

Workflow state, current step, and shared notes persist across every AI on the task.
No hand-off document. No copy-paste. The task itself is the hand-off.
Each AI starts with full context — not a blank page.

One audit trail across every AI

Every AI action logged with the AI that performed it.

Governance teams hate multi-AI setups for a reason — per-AI dashboards, per-AI logs, no single source of truth. ConvOps stitches every action into one audit trail, tagged with the AI that did it. "Which AI drafted this step? Which AI approved that one?" becomes a dashboard query. Per-AI logs die. Per-task audit trail takes their place.

In practice

Every step logged with the AI that drove it — field-level diffs included.
One audit trail per task, no matter how many AIs touched it.
"Which AI did what" is a query, not a forensic investigation.

Process outlives people — and models

New AI on Monday. Same workflow on Tuesday.

Process outlives people. It also outlives models. New frontier model arrives, compliance approves a new vendor, a custom agent gets promoted into production — the workflow does not care. Swap the AI on any step. The process stays. The shared notes stay. The audit trail stays. The model choice becomes a detail.

In practice

Swap the AI per step. The workflow does not notice.
Process is the durable asset. The AI assignment is the mutable one.
People leave. Models change. The process keeps running.

See it in motion

See a team of AIs drive one workflow

Watch a blog post move across steps — research, draft, SEO review, publish. Different AIs can drive different steps of the same workflow, sharing one set of notes and one audit trail.

Workflow

Blog Post

Brainstorm Topics

Generate topic ideas based on the content brief and audience.

Research

Gather data, stats, and quotes to support the chosen topic.

Write Draft

Write the full blog post following the company style guide.

SEO Review

Gate

Check keywords, meta description, headings, and internal links.

Publish

Final formatting and publish to the company blog.

0 of 5 steps

Brainstorm Topics

Research

Write Draft

SEO Review

Publish

Claude

Watch the workflow in action

0 / 5

Each AI in its silo vs every AI on the same workflow

Each AI conversation starts blind

Every AI works in its own silo — no shared state, no shared notes, no hand-off.
Hand-off between AIs is copy-paste and pray.
No record of which AI did what, when — per-AI dashboards only.
Multi-agent means building orchestration code or adopting a framework.
Swap an AI and the process leaves with it.

Each AI picks up where the last one left off

Every AI on the same task reads the same workflow state and the same shared notes.
The next AI drives its step with every previous action already documented.
One audit trail across every AI — who did what, when, and what changed.
Per-step AI assignment is a config field, not a framework.
Swap an AI mid-workflow. The process stays. The notes stay. The audit trail stays.

Questions

Frequently asked questions

They do not collaborate peer-to-peer, and ConvOps does not pretend they do. The workflow is the shared ground. Each AI drives the step assigned to it, reads the shared notes written by the previous AI, writes its own notes, and advances the workflow. The collaboration lives in the workflow, not in the AIs.

Because the context does not live in the AI. It lives in the task — the workflow state, the shared notes stream, the audit trail. When a new AI picks up a step, it reads the same notes the previous AI wrote. No summary. No hand-off document. The actual working memory.

Even one-AI teams often use subagent capabilities — Claude Code has subagents, for example. ConvOps lets you assign which subagent drives which step. And when you adopt a second AI for a specific job — a compliance-approved model, a coding-specialized agent — the workflow is ready. You do not have to rebuild.

Only if you want to. By default the AI that created the task drives the whole workflow. Per-step assignment is opt-in — useful when you have clear reasons (best model per job, cost optimization, compliance routing). The default works for most teams.

Frameworks build agents in code — an engineering project. ConvOps structures the conversations agents have, and lets any AI that can call tools run through the same workflow. No framework to learn. No orchestration code. Multi-agent as a process, not a codebase.

Point it at the same workflow. If it can call tools, it runs through ConvOps. Swap it in on any step — the workflow does not move, the notes do not move, the audit trail does not move. The model choice becomes a detail the process absorbs.

Yes. Engineering can run the drafting step with Claude, marketing can run it with ChatGPT, compliance can run the review with a custom agent — all on the same workflow, the same task, the same shared notes. The audit trail records which AI did which step, per team.

Continue the tour

Explore the rest of the platform.

Bring Your Own AI

Works with the AI your team already uses. Swap the AI. Keep the process.

Your Process, Every Conversation

Define the process once. Every AI on every step follows it.

ConvOps