AI Workflow

How It Works

The problem with disconnected AI automation

AI agents can perform useful tasks, but many companies still operate them through isolated prompts, personal scripts, and disconnected point automations.

The process around the agent is often invisible. Teams cannot easily see which context was used, which actions ran, why a step failed, where people intervened, or how much AI usage and cost is accumulating across teams.

Without a shared workflow and observability layer, every new automation creates another hard-to-review path through company systems.

How AI Workflow solves it

AI Workflow turns a selected process into an explicit, inspectable workflow. Teams define triggers, context, agent tasks, deterministic commands, review steps, notifications, approvals, and outputs as connected stages.

The workflow can clarify incomplete work, route outcomes through different paths, coordinate parallel review, notify the right people, and stop safely when required evidence is missing. Its dashboard can trace each action and connect workflow versions, agent and model usage, human interventions, outputs, and cost.

Instead of replacing the tools your teams already use, the workflow coordinates them around a repeatable operating process. Teams can configure and extend the open-source project themselves, or work with Blazity when they want help with discovery, integrations, controls, deployment, or rollout.

Architecture overview

1. Trigger and integration layer

Selected events from your existing systems start the workflow. Custom integrations normalize the input and load the relevant project context, company rules, permissions, and workflow data before execution begins.

2. Workflow orchestration layer

The workflow definition controls branches, loops, parallel steps, input and output bindings, retry budgets, failure routes, agents, tools, notifications, and human checkpoints. Reusable patterns capture working practices without forcing every team into the same process.

3. Execution and control layer

Selected AI agents and deterministic tools operate with scoped access. Checks, budgets, approvals, independent review, and structured outputs control what each stage may do and whether the workflow can continue.

4. Observability and management layer

Teams can inspect active and historical runs, answer clarification requests, approve consequential actions, review evidence, and stop work when needed. Dashboards can aggregate workflow health, action traces, agent and model usage, cost, interventions, and outcomes across the configured organization scope.

Data flow

Event or approved manual action received → Relevant company context loaded → Custom workflow and policies selected → Agents, tools, and deterministic steps run → Checks, approvals, notifications, and failure routes applied → Output delivered to the existing system → Actions, usage, interventions, and cost recorded in the dashboard.

Get Started

Option 1: Try the open-source project

Explore the public repository, follow the setup documentation, inspect the workflow definitions, and run AI Workflow in your own environment.

Option 2: Configure it for your team

Start with one bounded process, connect the required systems, then configure the workflow, agents, checks, permissions, approvals, notifications, failure paths, and dashboard views around it.

Option 3: Work with Blazity

If you want implementation support, Blazity can help map the process, build missing integrations, configure controls, deploy the platform, test representative scenarios, and roll it out across additional teams and use cases.

Common Extensions

• Issue and incident triage with evidence-backed routing and escalation
• Code review, pull-request quality, browser regression, and release-validation workflows
• Team notifications, approvals, scheduled reports, and cross-system handoffs
• Content, data-processing, customer-support, and other repeatable operational workflows
• Additional company-specific integrations, permissions, security controls, and deployment profiles
• Organization analytics connecting workflow versions, AI usage, cost, intervention, quality, latency, and outcomes

Outcome

AI Workflow gives organizations a shared control and observability layer for AI-assisted work.

Agents can move quickly, but the surrounding process stays explicit: the right context is loaded, actions are scoped, checks are enforced, humans approve consequential decisions, notifications reach the right teams, and every run leaves evidence.

The result is not another isolated agent. It is an open, adaptable operating layer for repeatable workflows that teams can inspect, govern, and improve while leaders see AI usage and cost from one place.