Results for “agent-workflow”

AI Agent Workflow & Skill Architecture Guide — turn expert work into measurable, reusable agent systems. Covers workflow discovery, skill decomposition, harness design, evaluation, human escalation, observability, cost control, and multi-agent orchestration. Includes a measured marketing-delivery case: 15 people × 3–4 weeks reduced to one strategist + AI in 5 days. By Gingiris.

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Guide a scoped coding task through specification, implementation, and review. Set acceptance criteria, verification checks, iteration limits, and approval gates, then correct identified gaps until the requirements pass or a stopping condition is reached.

• Build a small feature from specification through verification
• Modify a module against explicit acceptance criteria
• Repeat fixes and checks within an iteration limit

Design systems where multiple AI agents share work, generate tool schemas that define tool inputs for Anthropic and OpenAI, and analyze execution logs. Outputs include role assignments, communication diagrams, and recommendations for addressing cost, latency, and failures.

• Designing agent roles for research automation
• Choosing how multiple agents coordinate work
• Generating and validating tool schemas

Design workflows in which AI agents handle multiple steps or specialist tasks. Choose a suitable structure, generate starter configurations, and define information handoffs, retries, validation checks, and limits on cost and context size.

• Choosing between one agent and a multi-agent workflow
• Generating a starter configuration for a multi-step pipeline
• Defining information handoffs and error recovery rules

Multi-agent workflow examples to work together on the OpenServ Platform. Covers agent discovery, multi-agent workspaces, task dependencies, and workflow orchestration using the Platform Client. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.

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Use for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first content changes, validation, cleanup proof, or durable workflow rules. Best when agents must prove the actual folder/module/scene/prefab/runtime owner before editing, especially runtime UI, generated assets, code graphs, tutorial/state flows, guided selection/action flows, overlay/dim source-bound mistakes, coordinate conversions, focus/highlight/marker/HUD alignment, predictive lead aiming, ballistic trajectories, spatial IoU verification, or repeated "fix still not visible" failures.

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Design multi-agent orchestration with workflow DAGs, routing, handoff protocols, and state management. Use when building pipelines of specialized agents, designing fan-out/fan-in patterns, or implementing fault-tolerant workflows.

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Sets up how you and your team build and work with AI agents, and produces an Agent Workflow Charter (agent lanes, plan-mode loop, autonomy ladder with promotion criteria, rules file, review policy, security rules, rollout plan). Use when you ask 'how should we work with Claude Code / Codex / Cursor', 'how many agents should I run', 'should the agent be autonomous', 'plan mode', 'CLAUDE.md', 'AI-native team', 'roll out AI to my team', or 'my agent keeps screwing up'. Draws on 110 Lenny's Podcast guests incl. Claire Vo, Boris Cherny and Aishwarya Naresh Reganti.

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Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.

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Converts repeated AI coding-agent workflows into reusable skill packages with triggers, guardrails, examples, and verification checks. Use when a user wants to turn a prompt, checklist, AGENTS.md section, Claude/Codex workflow, or recurring agent task into a portable SKILL.md.

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AI agent workflow patterns including ReAct agents, multi-agent systems, loop control, tool orchestration, and autonomous agent architectures. Use when building AI agents, implementing workflows, creating autonomous systems, or when user mentions agents, workflows, ReAct, multi-step reasoning, loop control, agent orchestration, or autonomous AI.

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Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates building-agents-* and testing-agent skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.

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Guide plan → instrument → execute → validate with explicit checkpoints and questions. Use for ambiguous tasks or when enforcing a consistent agent workflow.

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Use when planning a source-grounded method overview, architecture, pipeline, system/data-flow, mechanism, or agent-workflow figure for a CS or deep-learning paper. Produces an editable figure brief before image generation or manual redrawing; not for plots, tables, or general prose.

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Build reusable agent workflow patterns — subagents with scoped briefs, event hooks, slash commands, and session management. Use when turning one-off agent tasks into repeatable, composable workflows.

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Use for agent workflow collab recipes when optimizing a Coworld player.

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Use for Crewrift-specific agent workflow collab recipes when optimizing a player.

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Use for agent workflow collab recipes in scripted Coworld policy optimization.

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Use for reusable agent workflow collab recipes in software and coding-agent work.

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Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.

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Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).

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Use when designing retryable or crash-resilient agent workflows, especially when tools can send, charge, publish, provision, or otherwise create duplicate side effects.

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Use for the monthly competitor-discovery radar over spec-driven and agent-workflow tools.

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Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous work.

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