Case study
AI Automation Engine
Built a platform where a plain-language request becomes a fully-parameterized, runnable pipeline — the system plans the steps, fills in the parameters, and can act through a browser, a terminal, or any connected tool, healing itself when a step breaks.
Overview
The client needed automation that started from how people actually describe work — a sentence, not a form — and turned that into a real, executable pipeline without an engineer translating it by hand.
The Challenge
Most workflow builders make a person manually wire up every step, trigger, and parameter in a visual editor. None of them start from someone just describing what they want and correctly work out both the steps and the specific values each step needs.
The business impact:
- Building even a simple automation meant manually configuring steps, triggers, and parameters in a workflow editor.
- A description like "when a new lead comes in, research them and notify the team" had no path to becoming a working pipeline without someone translating it by hand.
- Ambiguous or missing details in a request meant the automation would run with wrong or guessed values, or fail outright.
Approach
How do you turn a plain-language request into an actual multi-step pipeline?
A one-sentence description doesn't map cleanly to a sequence of steps. We built an intent-and-planning layer that classifies what the user is asking for, routes simple requests to a fast path and complex ones to a full planner, and drafts a normalized, step-by-step routine from the request — asking a clarifying question first if the request is genuinely ambiguous.
How do you fill in the right parameters for each step, automatically?
Even a correct plan is useless if its steps are missing the specific values they need. We built a parameter layer that analyzes each step's goal, generates the parameters it needs, enriches them with conversation and integration context, resolves relative dates and times, and validates everything before the routine runs — correcting a step's parameter to reference an earlier step's real output where the plan calls for it.
How do you let that pipeline actually act, not just plan?
A generated plan still has to execute against real tools. We built execution paths that can drive a browser, control a terminal, or call any connected integration, with a human-in-the-loop approval step before anything runs when the request calls for it.
How do you stop one broken step from taking down the whole pipeline?
A single failure used to mean the whole automated run stopped and someone got paged. We built a self-healing layer: an error detector flags the failure, an issue classifier identifies the type of problem, a patch generator proposes a fix, a safety validator checks it's safe to apply, and a post-heal verifier confirms the fix worked, escalating to a human instead of guessing when it can't safely repair itself.
Tech Stack
Outcome
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