/agents-best-practices

name: agents-best-practices
description: "Use this skill when designing, generating an MVP blueprint for, auditing, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, permissions, system prompts, planning, goals, context compaction, memory, skills, MCP/external connectors, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety."
metadata:
  version: "1.3.0"
  scope: "provider-neutral-agent-harness"
  file_policy: "markdown-only"

Agents Best Practices

Use this skill when the user asks how to build, improve, debug, or evaluate an agentic harness. This is a general-purpose agent architecture skill. Coding agents are one subdomain only; apply the same principles to research, finance, legal, support, operations, sales, healthcare, education, data analysis, procurement, and workflow automation agents.

Core stance

An agent harness is the control plane around a model. The model proposes actions; the harness validates, authorizes, executes, records, summarizes, and returns observations. Keep the loop simple and make the runtime rigorous.

Default architecture:

user/task
  -> instruction and context builder
  -> model call
  -> tool/action proposal
  -> schema validation
  -> permission decision
  -> execution or approval pause
  -> structured observation
  -> context update
  -> repeat within budget or finish

When to activate this skill

Use this skill for prompts involving any of these intents:

• build an agent, agentic workflow, AI worker, autonomous assistant, or harness;
• create a domain-specific MVP agent design, starter harness, implementation blueprint, or first production-safe version;
• choose between OpenAI, Anthropic, OpenAI-compatible APIs, direct tool loops, hosted tools, or SDKs;
• design tools, permissions, guardrails, approval flows, or sandboxing;
• create planning mode, workflow orchestration, goal mode, todo tracking, or long-running task behavior;
• add context compaction, memory, retrieval, scoped instructions, or prompt hierarchies;
• design a recursive language model (RLM), programmable-context runtime, self-refining or continual harness, retained child agents, daemon-backed or scheduled agent, or executable skills;
• attach Agent Skills, reusable workflows, MCP servers, external connectors, or tool search;
• audit an existing agent for reliability, cost, prompt-cache hit rate, safety, latency, or observability;
• create system prompts or developer instructions for a domain-specific agent;
• make source-of-truth knowledge, validation signals, logs, metrics, or workflow state legible to an agent.

Do not use this skill for ordinary single-turn writing, translation, or Q&A unless the user is asking about the design of an agent that will perform those tasks.

How to use this skill

First, identify the user's design problem:

1. **Domain**: what work the agent performs. 2. **Autonomy level**: answer-only, draft-only, approval-gated action, or autonomous action within policy. 3. **Risk level**: read-only, internal write, external communication, financial, legal, healthcare, security, destructive, or privileged. 4. **State duration**: single turn, multi-turn session, resumable workflow, or long-running goal. 5. **Tool surface**: internal APIs, hosted tools, MCP/external connectors, browser, sandbox, filesystem, database, communication, or computation. 6. **Validation**: what proves the task is complete.

Then load the most relevant reference files, not all files by default. If the user asks to make or build an agent for a domain, default to MVP Builder Mode.

MVP Builder Mode

When the user asks to make, build, design, scaffold, or specify an agent for a domain, produce a concrete domain-specific MVP harness blueprint, not only advice. Use [mvp-agent-blueprint.md](references/mvp-agent-blueprint.md) as the primary reference and load other references as needed.

Default behavior:

1. Infer a reasonable first version from the user's domain and stated constraints. 2. State assumptions briefly instead of blocking on missing details. 3. Design the smallest safe harness that can accomplish useful work. 4. Include the core agentic loop, tool registry, permission matrix, context/memory/compaction, planning mode, goal-like loop criteria, skills/connectors, prompt-cache/cost strategy, observability, evals, and launch path. 5. Mark high-risk actions as draft-only or approval-gated by default. 6. Keep the MVP to the smallest reliable single-loop harness unless the user explicitly asks for a broader architecture.

Advanced Recursive and Continual Harness Mode

Use this mode only when the user explicitly asks for programmable context, recursive execution, retained children, continual refinement, executable skills, or daemon/scheduled autonomy. Treat it as post-MVP: establish a measured single-loop baseline first, then read [self-refining-recursive-harnesses.md](references/self-refining-recursive-harnesses.md) together with the context, workflow, permission, security, and eval references.

Make the context representation, recursive unit, mutable state, promotion scope, lifecycle, budgets, validation probes, and rollback path explicit. Keep base authority, permission enforcement, credentials, budgets, and evaluation policy outside the mutable surface.

Reference map

• Read [mvp-agent-blueprint.md](references/mvp-agent-blueprint.md) first when the user asks to create a new domain-specific agent or MVP harness.
• Read [coding-agents.md](references/coding-agents.md) when the requested agent reads, edits, tests, reviews, migrates, or opens changes against a software repository.
• Read [architecture.md](references/architecture.md) for the full harness model and component boundaries.
• Read [agent-legibility-fe