Give the agent a way to verify success
Use tests, linting, screenshots, or command output as explicit acceptance checks. This is the clearest recommendation from Anthropic and it is reinforced by ecosystem projects that build test runners, deploy checks, or browser QA into the workflow.
Sources: https://code.claude.com/docs/en/best-practices · https://github.com/undeadlist/claude-code-agents · https://github.com/nizos/tdd-guard
Separate exploration, planning, and implementation
The most mature Claude Code workflows avoid mixing research and coding in one uninterrupted stream. Plan mode, specification docs, mission systems, and issue-driven execution all reduce drift.
Sources: https://code.claude.com/docs/en/best-practices · https://github.com/obra/superpowers · https://github.com/ayoubben18/ab-method · https://github.com/automazeio/ccpm
Use parallelism only with isolation
Running many agents is useful when each has a crisp role or isolated workspace. Worktrees, tmux sessions, issue scoping, and phase gates are recurring patterns in the strongest orchestration tools.
Sources: https://github.com/avifenesh/agentsys · https://github.com/automazeio/ccpm · https://github.com/smtg-ai/claude-squad
Invest in visibility into context, usage, and session state
Long-running agent work degrades when context gets bloated or when users lose track of active tools and subagents. Status lines, session search, and history/diff UIs are not cosmetic; they are operational tooling.
Sources: https://code.claude.com/docs/en/best-practices · https://github.com/jarrodwatts/claude-hud · https://github.com/pchalasani/claude-code-tools · https://github.com/andrepimenta/claude-code-chat