Make data and AI useful
Automate multi-step work, with people approving what matters
We build workflows where AI coordinates steps, uses only approved tools, checks intermediate results and escalates the decisions that need human judgment.
In short
Agentic workflow automation lets AI coordinate several steps of a business process. It works within defined responsibilities and permitted tools, validates intermediate results, and asks a person to approve consequential or irreversible actions. Autonomy increases only when testing and risk assessment justify it.
Last updated
01 The problem
What problem does this solve?
Some business processes span several steps and several systems, such as reading a request, looking something up, updating a record and asking for an approval. People carry the work from one step to the next by hand.
Handing that to AI raises fair questions. What is it allowed to do? What happens when it is wrong? Who approves anything that cannot be undone? Without clear limits, an automated workflow adds risk instead of removing effort.
02 What we build
What do we build?
- A workflow with defined tasks, limits and explicit states
- Approved tool interfaces to your business APIs and records
- Validation of inputs, intermediate results and outputs
- Bounded retries, timeouts and budgets
- Human approval gates for consequential actions
- Escalation and safe-failure paths
- Execution traces, cost and failure tracking
03 Architecture
What does a typical architecture look like?
04 How it goes
How does the work usually go?
-
Check that AI is needed
We map the process and test the assumption that it needs AI, because plain software or rule-based automation can be the more dependable choice.
-
Define tasks, tools and limits
We write down the responsibilities, the approved tools, the permissions and the termination conditions before anything is built.
-
Build the workflow with controls
We implement orchestration with explicit states, validation, bounded retries and timeouts, and human approval gates.
-
Evaluate and pilot
We test tool selection, task completion and failure scenarios, including prompt injection and unauthorized tool use, in a time-boxed pilot with success criteria.
-
Release and widen with care
We release with traces and monitoring, and increase autonomy only when testing, requirements and risk assessment justify it.
05 Capabilities and stack
Which capabilities and technologies are involved?
Capabilities
- Python
- FastAPI
- LangGraph
- LangChain
- CrewAI
- Amazon Bedrock
- Google Vertex AI
The final stack follows your requirements and what you already run. How we choose technology
06 A typical situation
What does this usually look like?
A support or operations team handles requests that need information from several systems before anyone can decide what to do, and each case passes through several hands.
We automate the gathering and preparation steps first, keep a person approving anything consequential, and review the execution traces to decide whether the workflow should take on more.
07 Questions
Questions people ask
You approve them. It uses permitted tools with least-privilege access, and anything consequential or irreversible waits for a person to approve it. How much freedom it gets grows only as testing and risk assessment support it.
Then we recommend it. A simpler rule-based workflow is easier to test and to explain, and we would rather tell you that than add AI for its own sake.
Controls include least-privilege access, input and output validation, and protection against prompt injection and unauthorized tool use. We test these as part of the evaluation, and failures are designed to end in a safe fallback or an escalation.
09 Terms
Which terms come up here?
If this sounds like you: Hand the legwork to an agent, keep the decisions