The full path to automation that survives the real world — wire the integrations and triggers, design the control API, move the data through validated stages, evaluate the AI steps, then deploy.
Workflow automation is where the demo and the reality diverge hardest: a chain of steps that works once on a clean input quietly loses data the first time a webhook double-fires, a third party times out, or an AI step hallucinates a value the next step trusts. This project builds an automation system that holds up — orchestration, not a single prompt. It starts where automations actually break: the integration boundaries and triggers connecting your systems, then the API that drives the flow, the data pipeline that moves and validates what passes through, the evaluation that proves the AI steps behave, and the deployment that ships it with rollback and monitoring. It owns workflow orchestration specifically — not a conversational support agent, not a research assistant, not a plain API backend — so every decision serves a reliable multi-step flow across systems. Each stage connects to a NewPrompt workflow you can run on its own; together they turn a fragile script into a system you can trust to run unattended. You own the business logic; the project makes sure the orchestration is built for failure, not just the happy path.
Each stage runs a NewPrompt workflow, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.
See the execution map →Map the integrations and triggers
Start where automations break: the events, webhooks, and external systems the flow connects. Design the contracts and the failure handling — retries, idempotency, what happens when a third party is down — before wiring anything.
Design the control API
Design the API surface that drives and controls the automation — how flows are triggered, configured, and observed — so the orchestration is something you operate, not a black box of cron jobs.
Build the data pipeline
Design how data moves through the automation — ingestion, transformation, and the validation gates that stop a bad value from propagating through every downstream step and corrupting the result.
Evaluate the AI steps
The AI steps in the flow are non-deterministic and the steps after them trust their output. Build scenarios with expected results, catch the failures and hallucinations, and set a regression guard before the automation runs unattended.
Deploy and monitor the automation
Ship the automation the careful way — readiness checked, rollback planned, and monitoring on the signals that tell you a flow silently stopped working — because an automation you can't observe is one you'll only fix after it's been broken for a week.
An automation system built for the real world — integrations and triggers mapped with failure handling, a control API, a data pipeline with validation gates, AI steps evaluated against real scenarios, and the whole thing shipped with rollback and monitoring — an orchestration you can leave running instead of a script that loses data the first time something hiccups.
No — it designs a real automation system with a control API and a data pipeline. AI accelerates the design and the AI steps inside it, but you wire and own the integrations.
The AI steps. This project is about making automation that includes model calls reliable — evaluating those steps and gating the data so a bad model output does not corrupt the whole run.
Map the integrations and triggers first. The journey is ordered so each stage — control API, data pipeline, evaluation, deploy — builds on the one before it.
An MVP should automate one multi-step flow end-to-end, not every process at once. Wire the real integrations and triggers with failure handling, drive it through a control API, put validation gates in the data pipeline, and evaluate the AI steps against scenarios before you leave it running unattended.
Reliability comes from designing for failure, not the happy path. This blueprint builds idempotency and retries into the integration boundaries, validation gates that stop a bad value propagating downstream, evaluation with a regression guard on the non-deterministic AI steps, and deployment with rollback and monitoring.
No. NewPrompt gives you the structured path, prompts, and workflows to design the integrations, contracts, and failure handling; you wire the actual connections in your own tools or platform and own every trigger. The blueprint makes sure those boundaries are built for double-fires and timeouts, not just clean inputs.
Connect systems so they don't break each other — map the integration boundaries, design the event and webhook contracts, plan retries and failure handling, then document the integration.
Design an API on its contract instead of discovering it endpoint by endpoint — model the resources, design the endpoints and payloads, pin the contract, then review it before code locks it in.
Design a pipeline that moves data without corrupting it — map the sources and ingestion, design the transformation stages, set validation and quality gates, then document the pipeline and monitoring.
Find out whether an AI agent behaves before users do — define what correct means, build test scenarios with expected outputs, catch failures and hallucinations, then regression-test each version.
Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after.
You have the schema — fields, types, requirements. The translation into a prompt the model actually follows: schema lines, realistic example, and validation rules.
Architecture review for the public surface: abstractions that earn their place, dependency direction, seams, and the patterns the codebase already has.
Fields checked against the contract: missing ones flagged, invented ones caught, prose around the object detected.
Required fields one at a time, invalid formats, business rules at their exact boundaries — validation tested the way users break it.
SHIP or DO NOT SHIP: twelve checks on failure modes, observability, rollback, and load — the review that happens before the incident.
Build the test set an agent has to pass — scenarios across the happy path, edges, and adversarial inputs, each paired with the expected behavior to grade against.
Make sure a fix didn't break three other things — compare an agent's outputs across two versions on the same scenarios and flag every behavior that changed for the worse.
Build prompts that return structured data — JSON first, with YAML, XML, and CSV modes — parseable every time.
Break a big goal into a sequential prompt workflow — each step with an objective, prompt, and expected output.
Paste an AI's output and validate it against the expected format — with a repair prompt for every failure found.
Build test generation prompts — unit, integration, or E2E — with framework modes and edge-case coverage rules.
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
The full path to a backend you can put clients on — define the requirements, design the architecture, API contract, data model, and access control, then build it reviewed, tested, secured, and shipped.
The full path to a support agent you can put in front of customers — write its instructions, ground it in your docs, route and handle tickets, then evaluate and cost-control it before it goes live.
The full path to a pipeline that moves data without corrupting it — design the ingestion and transforms, extract and structure the sources, gate the quality, store it, then deliver and ship it monitored.