$ compare --workflow-engines --agent-frameworks
Not another workflow engine. A release gate for AI-built change.
AI can already draft a firewall policy, a Terraform change or a Kubernetes manifest. The hard part is trusting it enough to apply. pAIchart lets the AI plan the change, then refuses to release it unless checks the AI cannot fake show it is safe.
01 / at a glance2×2
Who builds the workflow, and is its output checked?
Most systems either run a fixed workflow with trusted steps, or let AI steer without checking its work. pAIchart combines AI-built workflows with checks before release.
02 / who it is forthe approver
Built for whoever approves risky infrastructure change.
The promise to each is the same: AI drafts the change, and the platform shows it is safe before a person approves it. Device vendors and infrastructure-as-code platforms are integration partners: pAIchart works alongside their tooling.
| Team | Change they approve | Why verification matters |
|---|---|---|
| Network operations | Device configuration, routing and firewall policy across many devices | Change is still largely manual, and one misconfiguration can take a site down |
| Platform engineering and SRE | Terraform plans, Kubernetes manifests, observability config | AI-drafted changes need checks that hold before apply, not after |
| Change advisory boards in regulated industries | Any production change | Approval needs an audit trail of checked facts, not an AI’s own summary |
| Managed service providers | The same kinds of change for many customers | One verified process applies across every customer estate |
03 / the landscape15 systems
The well-known workflow and agent systems.
Workflow systems that run steps as a dependency graph fall into four groups. Most assume that a step which finishes produced a correct result.
| System | Maker | Category | Who defines the workflow |
|---|---|---|---|
| Apache Airflow | Started at Airbnb; now the Apache Software Foundation | Data and workflow orchestration | People, in Python code |
| Luigi | Spotify | Data and workflow orchestration | People, in code |
| Dagster | Dagster Labs | Data orchestration, asset-centric | People, in code, with typed data assets |
| Prefect | Prefect | Data and workflow orchestration | People, in Python code |
| Argo Workflows | Started at Intuit; now CNCF | Kubernetes-native workflows | People, in YAML |
| dbt | dbt Labs | Analytics transformation | People, as SQL models |
| Temporal | Temporal Technologies (from Uber’s Cadence) | Durable execution | People, as code that survives failures |
| AWS Step Functions | Amazon Web Services | Managed state machines | People, in a JSON state language |
| Netflix Conductor | Started at Netflix; now Orkes | Microservice orchestration | People, as JSON definitions |
| LangGraph | LangChain | AI agent graphs | People define the graph; the AI chooses the path |
| Microsoft Agent Framework | Microsoft (successor to AutoGen and Semantic Kernel agents) | AI agent framework | People define agents; the AI coordinates them |
| CrewAI | CrewAI | AI agent teams | People define roles; the AI coordinates them |
| OpenAI Agents SDK | OpenAI | AI agent handoffs | People define agents; the AI hands off between them |
| Agent Development Kit (ADK) | AI agent framework | People define agents and flows | |
| GitHub Actions, GitLab CI, Tekton | GitHub, GitLab, CD Foundation | CI/CD pipelines | People, in YAML |
Product descriptions are summarised from public information available up to mid-2026 and may lag recent releases. Names are trademarks of their owners.
04 / how pAIchart compares9 dimensions
Two differences: the AI writes the workflow, and no output is trusted until it is checked.
| Dimension | Classic orchestrators | AI agent frameworks | pAIchart |
|---|---|---|---|
| Who builds the workflow | People, in code, before it runs | People define the graph; the AI picks a path through it | The AI plans the steps and their dependencies at run time; the platform runs them |
| How it runs | A central scheduler, or deterministic replay (Temporal) | Usually an in-process loop | Event-driven: each completed step triggers the steps that depend on it, from durable database state |
| Data passed between steps | Trusted | Trusted shared state | Treated as untrusted: quarantined, sanitised, size-capped and labelled with where it came from |
| Is a step’s output correct? | Success means the step finished; Dagster asset checks and dbt tests check data quality | Mostly evaluated offline, after the run | Finishing is not enough: automatic checks recompute derived values, reviewer verdicts are recorded as structured facts, and release requires every required check to pass |
| Human approval | Sensors, callbacks, signals | Interrupts | Approval gates are steps in the graph that only a person can complete |
| Workflows of workflows | Sub-workflows, child workflows | Sub-graphs | Programs made of pipelines, with a binding interface contract between them |
| Recovery from failures | Very strong; Temporal’s is the benchmark | Limited | Built for this workload: stuck steps are detected and closed out so a program cannot hang silently |
| Scale | Millions of tasks a day | Varies | Built for low-volume, high-stakes change, not throughput |
| Ecosystem | Very mature, with large integration libraries | Growing quickly | Focused: network, Terraform, Kubernetes and observability change |
05 / where it fitsand where it does not
Use it where a wrong change costs far more than a refused one.
Use pAIchart for
- AI-drafted changes that need proof before approval: network policy across devices, Terraform plans, Kubernetes manifests, observability config
- Changes spanning several teams or systems, where one part’s output feeds the next and must arrive intact
- Approval processes that need an audit trail of checked facts rather than an AI’s own account of its work
Keep on existing tools
- High-volume data pipelines and scheduled ETL (Airflow, Dagster, Prefect, dbt)
- Long-running business processes that need durable execution at scale (Temporal, Step Functions)
- The apply step itself: pAIchart prepares and verifies the change, and your existing pipeline or operator applies it
pAIchart reads live state without changing it, and treats a refusal as a valid outcome: it says what is missing rather than releasing a change it cannot support.
06 / maturity todaywhat is validated
Single pipelines are validated end to end. Programs are being hardened.
| Area | Status |
|---|---|
| Network change | Validated against lab network devices; change packages applied with zero configuration-syntax defects |
| Terraform change | Validated against a local cloud emulator; the AI never sees Terraform state |
| Kubernetes and observability change | Running end to end on the same review and verification structure |
| Derived values (e.g. an address range computed from live state) | Approved in 5 of 5 consecutive runs, each automatically re-checked |
| Multi-pipeline programs | Run end to end; release rate is the current focus. Recent refusals were about incomplete change write-ups, never wrong configuration |
| Design partners | We are taking on a first design partner in network operations |
A public verification pack describes how each claim was tested.