Getting Started with Agent Loops in Claude Code

Claude Code now treats agent loops as the default way models keep working until they hit a clear stop condition. The approach replaces one-off prompts with structured repetition that fits different tasks.

Four loop types cover the main patterns developers need. Turn-based loops start from a user message and let the model decide when to stop or ask for more context. Goal-based loops use the /goal command to set verifiable completion criteria and a maximum number of turns. Time-based loops run on fixed intervals through /loop and can move to the cloud with /schedule. Proactive loops trigger from events or schedules without anyone watching in real time.

These loops matter because most knowledge work repeats the same checks. Writing SKILL.md files turns human verification steps into code the agent can run end to end. The file defines pass/fail rules so Claude can validate its own output before moving to the next turn. Teams that adopt this pattern cut the number of manual hand-offs by roughly half in early tests.

The practical change is that context becomes the bottleneck. A single agent loop can pull from meetings, documents, and past decisions only when those records sit in one searchable place. Without that base layer, the model repeats questions or produces outputs that ignore earlier choices.

Turn-based loops handle simple hand-offs

Turn-based loops fit tasks where the agent can judge completion on its own. The model receives a prompt, works, then either finishes or requests more input. This type works well for code reviews or first drafts where the stopping rule is obvious once the output meets a visible standard.

Developers set the loop by starting a conversation and letting the model continue until it signals completion. The main limit is that every turn still depends on the user staying in the session. When the task spans multiple days or requires external data, the loop often stalls.

Goal-based loops add measurable targets

Goal-based loops remove guesswork by requiring an explicit finish line. The /goal command accepts a verification condition and a turn cap. The agent then runs until the condition passes or the cap is reached. This structure suits regression checks, data validation, and report generation where success has a binary test.

Teams report that the explicit goal reduces scope creep. The agent stops proposing extra features once the defined criterion is satisfied. The trade-off is that poor goal definition produces loops that run to the maximum turns without useful output. Clear verification statements become essential.

Time-based and proactive loops run without constant oversight

Time-based loops execute on a schedule. The /loop command sets the interval, and /schedule can push the run to cloud infrastructure. Proactive loops go further by tying execution to events such as new files appearing or calendar triggers. Both types remove the requirement for a human to stay online.

These loops shift work to background processes. A daily summary agent can scan new documents every morning and post results to a shared channel. The remaining constraint is the quality of the context the agent carries between runs. Loops that lack persistent memory start each cycle with incomplete information.

SKILL.md turns manual checks into automated gates

The SKILL.md file is the practical bridge between human standards and autonomous runs. Writers encode review steps that previously required a person to open the output and compare it against a checklist. The agent then applies those rules on every loop turn.

A typical SKILL.md entry defines the expected output shape, required data fields, and a simple pass condition. Claude reads the file at the start of each turn and stops only when the condition holds. This pattern works for contract reviews, slide deck consistency checks, and spreadsheet formula validation.

Organizations that maintain SKILL.md files see fewer loops that end with “please review.” The cost is the initial effort to write the rules. Once written, the same file applies across projects that share similar output formats.

Context gaps limit what loops can achieve

Even well-structured loops produce weak results when the agent lacks access to prior decisions. A time-based report generator needs last quarter’s metrics, the current project brief, and any client feedback recorded since the last run. If those items live in separate tools, the agent must ask clarifying questions or guess.

remio stores meetings, documents, and browsing history in one searchable layer. When an agent loop runs inside that environment, it already holds the background that would otherwise require repeated prompts. The same SKILL.md rules can then operate on data that reflects actual team history rather than the last message in the thread.

Early patterns show both gains and friction points

Teams testing loops report faster turnaround on repetitive tasks. A goal-based loop handling weekly status updates now finishes in under ten minutes with no human edits in most cases. Proactive loops that monitor competitor sites surface changes before scheduled meetings.

The main friction remains rule maintenance. SKILL.md files must be updated when output requirements change. Outdated rules produce loops that either fail to stop or accept incorrect results. Version control for these files has become a new workflow step for several groups.

What to watch next

Three signals will show whether agent loops move from niche experiments to standard practice. First, adoption of shared SKILL.md libraries inside open repositories will indicate whether teams treat the files as reusable assets. Second, integration between loop schedulers and enterprise context stores will reveal whether memory limitations are being addressed at the platform level. Third, reported error rates on long-running proactive loops will test whether current verification methods scale beyond small tasks.

Developers and product teams already running repeated checks should test one goal-based loop this month. The setup cost is low, and the output gives a direct measure of how much context the agent needs to work without constant supervision.