/agents-best-practices

name: agents-best-practices
description: "Use when designing, auditing, refactoring, or explaining an agentic harness for any domain, especially when work must continue from a measured gap to verified completion. Covers provider-neutral loops, tools, permissions, environment adaptation, planning, durable workflow state, context, skills, observability, evals, and safety for OpenAI, Anthropic, and compatible APIs. Not for implementing an ordinary app feature or reviewing one concrete diff."

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

Completion and reconciliation

For an action request, treat a discovered problem as a state transition, not a terminal report. Before calling the request complete, reconcile every required item using the contract in [completion-reconciliation.md](references/completion-reconciliation.md). Diagnosis-only internal gaps re-enter owned work; final prose is never a completion signal.

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;
• design an agent for a partially known or changing environment using capability discovery, safe probing, runtime binding, schema verification, or drift invalidation;
• reduce code-mode or programmatic-tool latency through speculative execution, partial-program analysis, futures, exact claim semantics, or cancellation of unused work;
• 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.

Environment-Adaptive Tool Mode

Use this mode when the useful tool catalogue, schemas, versions, or implementations are late-bound rather than fully configured before the run. Read [environment-adaptive-tools.md](references/environment-adaptive-tools.md) together with the standard tool, connector, security, and eval references.

Require a small trusted bootstrap interface, host-owned capability ledger, provenance-labeled descriptors, bounded read-only or isolated probes, opaque scope-and-version bindings, call-time permission checks, and drift invalidation. Discovery, generated code, and inferred schemas must never grant authority. Keep this post-MVP unless adapting to cha