AI Agents Built to Handle Real Work, Not Just Demos

Building Model Context Protocol servers for internal systems, proprietary APIs, and databases with no existing MCP implementation, so agents call them through the same standard interface as any other tool, no bespoke connector code per integration.

Wrapping existing APIs and third-party integrations as MCP servers so agents discover and call them through the standard protocol instead of custom glue code, with versioning that prevents schema drift from silently breaking agent tool calls.

Building the memory architecture that gives the agent context across turns and sessions, short-term conversational memory, long-term persistent memory, and the retrieval strategy that surfaces the right context without overloading the model's context window.

Building agent systems using composable components: Skills extend behavior through reusable capability units, sub-agents decompose complex workflows across specialized agents, and hooks enforce guardrails and logging at points the model cannot bypass.

Designing systems where multiple specialized agents collaborate, with an orchestrator that decomposes tasks, routes work to the right sub-agent, and assembles results, including the failure handling for when one agent's output breaks the next step.

Building checkpoints where the agent pauses for human confirmation before high-stakes actions, covering which actions need approval versus which run autonomously, and the audit trail recording every human decision alongside the agent's reasoning.

Designing the prompts and reasoning patterns that determine how the agent plans, decides, and recovers from errors, chain-of-thought for complex tasks, self-reflection before returning output, and structured formats for downstream processing.

Instrumenting token usage per trace and designing agents to minimize unnecessary model calls, since multi-agent workflows that chain calls accumulate cost fast. Covers model selection by task, caching, and latency profiling to find what's slow.

Building controls that stop the agent from being manipulated through adversarial inputs or leaking sensitive data, covering prompt injection detection, output validation against a defined schema, and scope restrictions tied to user permissions.