AI Agents & Workflow Platforms

The useful part of agent engineering is often the system around the model: reliable access to business context, typed workflows, review boundaries, auditable artifacts and repeatable deployment.

• Built agent workflows using Eve and OpenClaw across internal and client operations.
• Designed and shipped the complete ActiveCollab integration from API client through CLI and MCP transports.
• Turned an inherited quoting prototype into a production-oriented agent workflow with explicit job and artifact contracts.
• Defined read-only access, human-review states, environment isolation and operational deployment boundaries.

• Authenticated ActiveCollab tools for projects, tasks, comments, attachments, users, companies and time records.
• CLI, stdio MCP and HTTP MCP interfaces over a shared integration layer, with installer and distribution workflows.
• Typed agent job, event, artifact and result contracts with deterministic local test workers.
• Auditable web and email intake, provider-independent storage and synchronized agent outputs.
• Pricing-mirror integration, mismatch flags and explicit needs-review states for AI-assisted quoting.
• Security hardening, continuous integration and deployment automation for production agent services.

• Read-only MCP capabilities built around explicit authentication and operational boundaries.
• Structured worker contracts that normalize model output before it becomes application state.
• Human attention represented as a first-class workflow state rather than an unstructured model response.
• Agent processes isolated from database and storage credentials through narrow, allowlisted interfaces.

• Made project context available to team AI assistants through authenticated, repeatable tooling.
• Created reusable foundations for production agent workflows beyond a single chat interface.
• Connected AI-assisted work to observable jobs, review states and downloadable artifacts.