Design voice AI agents with telephony, streaming recognition, LLM orchestration and speech output. Includes interruption handling, tool controls, handoff and release tests.
Build resumable LangGraph workflows with checkpoints, interrupts, durable storage, thread authorization and safe recovery from replay and uncertain writes.
Choose deterministic workflows, bounded AI steps or autonomous agents using a decision matrix for repeatability, cost, auditability and business risk.
Monitor AI agents with task traces, tool failure categories, reliability objectives, cost reconciliation and a privacy-aware troubleshooting playbook.
Compare supervisor, router and hierarchical multi-agent patterns, with delegation contracts, execution budgets, failure recovery and a practical selection checklist.
Design scoped AI agent memory with thread state, durable facts and semantic retrieval. Plan provenance, tenant isolation, corrections and forgetting.
Design AI agent approval workflows with exact action review, expiry, escalation, duplicate-safe execution and an auditable decision trail.
Compare MCP, function calling and REST APIs by integration layer, ownership, latency, governance and use case, with a practical enterprise decision matrix.
Build a bounded MCP API adapter with Python, tenant-aware permission checks, five core tests and an enterprise authentication and deployment checklist.
Understand MCP hosts, clients and servers, with a read-only Python example, enterprise trust boundaries, approval controls and a deployment checklist.
Let’s plan your next step
Tell us about your goals, current systems and priorities. We’ll help you define a practical scope and the next step.
