A Benchframe product

Agentic Workflow

A control plane for AI agents in software delivery, documentation, and operations.

Design the process in the browser without writing code. A team of AI agents plans, implements, and reviews the work. People approve at defined gates. Every action is audited, and nothing is ever written straight to main.

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BRIEF PLAN EXECUTE REVIEW DELIVER ACCOUNT CLOSED LOOP Human gate

01 · The gap

AI tools work for one developer. Delivery is a team process.

Single-developer AI tools are good at writing code. They do not understand team delivery: splitting work into tasks, running review queues, merging parallel changes, escalating to a person when something needs a decision. Today that orchestration is done by hand, usually by a senior engineer.

Automation platforms built before large language models have no concept of an agent team. Agent frameworks require code and are not ready for production work: no human gates, no audit trail, no version control integration as a first-class element.

02 · Positioning

What Agentic Workflow is

Agentic Workflow is the layer that decides which agent may run, on which repository, at which step, with whose approval, and with what audit trail.

  • It is not another coding assistant. Tools like Cursor, Copilot, and Claude Code work with one developer. Agentic Workflow orchestrates tools like these as executors of individual steps.
  • It is not a framework. There is no Python to write. The process you publish in the designer is the source of truth for execution.
  • It is not a general automation platform. It is built for one job: governed delivery of software, documentation, and operations work.

03 · Process

How it works

Every run is a closed loop.

  1. Brief

    A request arrives from GitHub, Microsoft Teams, the mobile client, or a schedule.

  2. Plan

    Human gate

    An agent breaks the request into tasks. A person reviews and approves the plan.

  3. Execute

    The engine fans out parallel paths. Each step runs on the agent and model assigned to it.

  4. Review

    Agents review the results and run correction loops with a bounded iteration count.

  5. Deliver

    Human gate

    The output is always a controlled artifact: a pull request, an issue, or a report. Nothing is written directly to the main branch.

  6. Account

    Every action records its input, permissions, result, cost, and audit trail.

04 · Capabilities

Six capabilities, one control plane

No-code process designer

Compose the workflow from blocks in the browser. The published canvas is the source of truth for execution.

Cloud execution engine

Parallel paths, fan-out and merge, correction loops, durable state. Runs survive restarts, including runs waiting at human gates.

Multi-provider agent registry

Claude, Codex, OpenAI, Azure AI Foundry, AWS Bedrock, local models through Ollama, and custom APIs. Mix providers per step. No vendor lock-in.

Human gates and safe outputs

People approve at defined decision points. Policy checks run before each step: permissions, owner approvals, and an egress filter. Every result is a controlled artifact, never a direct write to main.

Complexity routing

A lightweight router classifies each request and chooses between a fast single-model answer and the full pipeline with planning, fan-out, and review.

Telemetry, audit, and cost

Full audit trail and per-run cost tracking. You always know which agent did what, at what cost, and who approved it.

Connects to GitHub, Microsoft Teams, and local environments. Clients for iOS, macOS, and the web. Secrets stay in the native system store, never in a repository.

05 · Proof

Built in production, proven on itself

We build Agentic Workflow with Agentic Workflow. The numbers below come from our own daily use.

In production on AWS

The execution engine runs in the cloud with durable state and full telemetry.

Over 1400 changes merged to main since April 2026

Planned, implemented, and reviewed by a two-person team working with AI agents as permanent contributors. Since late June 2026, every change passes an autonomous AI review and a deterministic governance gate before it merges.

Applications built end to end by agents

iOS applications went from a short brief through planning, coding, review, and fixes to builds published automatically to TestFlight. A person approved the plan; no person wrote the code.

Agentic Workflow is pre-release. There are no public case studies yet. We are selecting a first group of design partners.

06 · Fit

Who it is for

Engineering organizations of roughly 20 to 200 developers that:

  • run their code on GitHub and their communication on Microsoft Teams,
  • already use AI coding tools individually and want them to work as a governed team,
  • need approvals, policies, and an audit trail before agents touch production repositories.

If you are looking for a fully autonomous developer that works without oversight, this is not that product.

07 · Demo

See it on your own repository

A 30-minute demo on a real workflow, not a slide deck. Write to hello@benchframe.com and we will set it up.

Request a demo