AI Automation & MCP Integration

Automate a complete handoff

Many business tasks span several systems: look up a record, interpret the context, prepare an output and send it to another application. Useful automation needs reliable integration around the AI step, including validation, error handling and a record of what changed.

This service is suited to support operations, internal assistants and repeated analysis or reporting workflows. My experience includes Python APIs, tool-using agents and LangGraph orchestration. MCP is an integration capability described here; the linked public projects illustrate related automation and agent work, rather than claiming every example is an MCP implementation.

Inputs, outputs and scope

Inputs

A workflow map, API documentation, authorized test access, representative records, approval rules and examples of failed or duplicated work.

Outputs

Tool or MCP interfaces where appropriate, workflow orchestration, integration APIs, validation, run records and deployment instructions.

Decisions to agree

Which steps are deterministic, where model judgment helps, which actions require review and how partial runs or repeated events are handled.

Technical approach

1. Map the workflow and permissions

   Identify each source and destination, the actor's allowed actions and the fields needed at each step. Define the success condition and manual fallback before connecting an AI model.

2. Create narrow tool interfaces

   Expose specific operations with validated inputs and predictable output structures. Use MCP when a shared tool interface is useful; use direct API calls when they are the clearer integration path. Keep secrets in server configuration.

3. Orchestrate and control actions

   Use a workflow or LangGraph state machine to sequence calls. Separate lookups from writes, add approval points and handle timeouts, rate limits and partial failures. Design repeated events so they do not create duplicate actions.

4. Keep execution observable

   Capture useful run identifiers, tool results and failure reasons without unnecessarily logging sensitive payloads. Make it possible to find the last successful step and decide whether a run can safely continue.

Delivery and evaluation

The first delivery should automate a narrow end-to-end path against test data. After confirming the input and output contracts, I would add failure handling, permissions and the user review flow before expanding to additional operations or environments.

Acceptance checks include correct field mapping, repeated-event handling, expired credentials, unavailable tools, partial completion and unauthorized requests. We also measure end-to-end duration and model cost where the model is involved. Documentation explains configuration, tool boundaries, operator recovery and the actions that still require a person.

Relevant public work

These project pages provide concrete examples of related engineering and identify prototype or research scope where applicable.

Questions about this service

Is MCP required for every AI automation?

No. MCP is useful when tools need a shared integration interface. A direct API integration or a deterministic workflow can be a better fit for a small, well-defined task.

Can a person approve actions before they happen?

Yes. Approval can be part of the workflow before a record is changed or an external action is taken. The review should show the proposed action, relevant context and any information needed to assess it.

How do you prevent the same event from running twice?

The design can track event or request identifiers and record completed operations. The exact strategy depends on the upstream APIs and the action, and repeated-event scenarios belong in the acceptance checks.