#Workflow Design

Choose rules, an AI-assisted workflow or an autonomous agent by checking task uncertainty, verification and consequences with a business decision table.

Change an AI agent’s priorities while work is running. Choose what pauses, continues or queues, and verify pending actions before switching direction safely.

Apply user corrections to AI agent work without losing valid results. Trace affected assumptions, files and checks before the agent continues its task.

Select AI tool results without losing evidence. Use a field-level reference for identifiers, errors, summaries and artifacts that agents can retrieve later.

Assess rising AI usage against accepted work, review effort and delays. Build an evidence record before expanding access or claiming team productivity gains.

Build an interactive product demo with coding agents. Define one user journey, label simulated behavior and test a resettable experience before showing it.

GPT-Live-1 separates live conversation from backend work. Compare delegation modes, duration pricing and pilot checks before choosing a business voice setup.

Map managed agent state across conversations, compute and business actions. Check what survives a restart with a responsibility table and recovery worksheet.

OpenAI Agents API moves the agent loop into a managed runtime. Compare environment choices, recovery limits and application duties before planning a migration.

Keep voice conversations useful during slow tool calls. Design progress updates, corrections and late-result handling around the task’s verified backend state.

Measure voice agent latency from speech to verified results. Use a timing dictionary to separate first audio, tool delays, playback and interruption recovery.

Field, note, task or activity log: an agent has to choose a write target. A framework that keeps reporting fields clean and context where humans read it.

Background agents get more done unattended and hand you a large diff to review cold. Four criteria decide which mode a given task actually belongs in.

Moving from prompting an agent to employing a persistent teammate changes identity, memory, standing tool access and who owns the unwatched outcome.