The tool boundary is where AI products become useful or fail.

Give the model tools it can understand and the business controls it can trust.

Tool-calling systems need more than API wrappers. The tool surface must express the work clearly enough for a model to use, while also giving the business boundaries around permissions, write actions, sensitive data, failure modes, and review.

Dan brings practical experience with harness engineering, tool design, OpenAI-compatible interfaces, browser automation, desktop automation, backend APIs, and regulated workflows. The work focuses on making the agent useful, testable, and supportable.

Tool design areas

• MCP server design for internal systems, files, APIs, databases, browsers, and desktop workflows.
• Tool schemas, descriptions, argument models, error surfaces, idempotency, and safe retry behavior.
• Read, suggest, write, approve, and escalate boundaries for AI-driven actions.
• Event-driven task execution for long-running research, data updates, summarization, analysis, and remediation.
• Observability for prompts, tool calls, outputs, latency, failures, and user approvals.

When to bring Dan in

• Your prototype works in a demo but is brittle in real use.
• Your agents call tools, but the calls are hard to debug or trust.
• Your team needs an MCP architecture before exposing internal systems to models.
• Your product needs a human approval model for AI-generated changes.
• Your company needs browser or desktop automation connected to AI workflows.