Agentic Workflows With Guardrails
AI agents that route, triage, draft and take multi-step actions, scoped to a defined job, logged end to end, and held to a human review step wherever a wrong answer is expensive.
AI agents that route, triage, draft and take multi-step actions, scoped to a defined job, logged end to end, and held to a human review step wherever a wrong answer is expensive.
An AI agent demo is easy. An agent your operations team will trust with real work needs limits, logs and a way to say no.
An agent asked to 'handle customer emails' has no clear edges, so nobody can test it or say when it got something wrong.
When an agent takes an action, nobody can see what it read, what it decided or why. That ends the pilot in any regulated business.
Without a review step on high-risk actions, one confident wrong answer to a customer or supplier costs more than the agent saved.
Each agent has one job, the minimum permissions to do it, and a record of everything it did.
Inbound email, tickets, complaints or requests classified and routed to the right queue with a reason attached.
First drafts of replies, summaries, reports and impact assessments, sent to a person to edit and approve.
Agents that look things up across systems and complete a sequence of steps, limited to the tools and data their job needs.
Review steps placed where risk is highest, with clear approve, edit or reject actions and the reason recorded.
Scope limits, input and output checks, blocked actions, spending and rate limits, and safe fallbacks when the agent is unsure.
Every input, decision, tool call and output logged, with a test set to check quality before and after each change.
We automate the narrowest useful job first, then widen it with evidence.
What the agent does, what it must never do, which data it can see and what a good answer looks like.
Tool permissions, guardrails, review steps and logging designed in before the first real case runs.
Run alongside people, compare results, and only reduce review where the numbers show it is safe.
| Type | How it decides | Use it when |
|---|---|---|
| Rules-based automation | Fixed if-then rules | The logic is stable and fully known |
| AI workflow | AI handles one step, such as classifying or extracting, inside a fixed flow | Inputs vary but the process is the same every time |
| AI agent (agentic workflow) | An LLM chooses the next step and which tools to use, within limits | The path depends on what it finds, and a human still reviews high-risk actions |
Most real work needs a mix. We use the simplest type that does the job, because simpler is easier to test, explain and maintain.
Exception Handling & Monitoring · Document & Data Extraction · Regulated Manufacturing
Software that uses a large language model (LLM) to decide which steps to take and which tools to use to complete a task, rather than following a fixed script.
A way of building AI systems that plan and act across several steps and systems. In practice, useful agentic workflows are narrow, permissioned and reviewed, not fully autonomous.
A person reviews, edits or approves the AI's output before it takes effect, at the points where a mistake would be costly. The review and its outcome are recorded.
Controls that keep an agent within its job: limits on data and tools, checks on inputs and outputs, blocked actions, and safe fallbacks when confidence is low.
Yes, with a defined intended use, risk assessment, logging, human review of quality decisions and change control on prompts and models. See our Regulated Manufacturing page.
The first conversation is free, takes about an hour and covers one process end to end. You keep the map whether or not you hire us.