If you watch how most engineers use AI today, it looks eerily like using Stack Overflow in 2012: type a request, skim the answer, fix it yourself, repeat. The models got radically better, but the workflow barely changed.
The catch is that modern AI agents can already work for hours without your supervision, yet your average user still prompts them one turn at a time. The missing piece isn’t a new model; it’s a new pattern: loops. Loops are what serious teams now use to turn Claude, GPT, and tools like Mira on Telegram into autonomous workers instead of smart autocomplete.prosus+3
This article will walk through what loops actually are, how they work under the hood, when they’re worth it, how to build one with any LLM, and how Mira gets the same effect for everyday life without any infra.
From One-Off Prompts to Autonomous Loops
The prompt-shaped bottleneck
A standard prompt is a single instruction: you ask, the model answers, and then everything stops. You decide what to ask next, whether the answer is good enough, and how to proceed. The AI has no initiative; it’s a tool, not a worker.
This pattern has a hard ceiling. You are the scheduler, planner, verifier, and reviewer. Every improvement requires your time. Modern agent frameworks exist specifically to remove that dependency.cogitx+1
What a loop actually is
A loop gives the AI a goal, not a one-off instruction, then lets it run the full cycle repeatedly until the goal is genuinely met or a stop condition fires. Under the hood, each cycle looks roughly like:gleecus+1
- Discover – infer what needs doing from the current state.
- Plan – decide the next concrete step.
- Execute – take that step (edit code, call tools, update state).
- Verify – check progress against a strict goal.
- Iterate – feed the result back in and repeat if not done.
Without a hard verify step, you don’t have a real loop; you have an agent that keeps agreeing with itself. Verification is the gate that turns repetition into progress.tecadrise+1
The Three Non‑Negotiables: Verify, State, Stop
Verify: the gate that makes loops useful
A loop is only as good as its verifier. The model that did the work is a terrible judge of its own output; left alone, it will happily rubber-stamp half-baked solutions. Real loops use external checks such as:dailydoseofds+1
- Tests and builds (code passes, build is green).
- Type checks or linters (no violations).
- Numeric conditions (metric above/below a threshold).
- Rubrics scored by a separate evaluator model.
Claude Code’s /goal mode, for example, runs an evaluator model (Haiku by default) against the conversation transcript every turn to decide whether a completion condition has actually been met.reddit+1
State: why loops learn instead of spin
Every iteration needs memory. Without state, the agent re-discovers the same failure and tries the same fix forever. Modern agent loops keep a compact state store: what has been done, what failed, and what’s next. When the loop wakes up tomorrow, it resumes instead of starting from scratch.cogitx+1
This state is also where the cost creeps in, because the full context—goal, code, changes, failures, notes—is resent to the model on every turn.gleecus+1
Stop conditions: protecting your wallet
A loop that never stops will happily burn tokens all night. Production systems ship with two stop conditions:
- Success: the verifier says the goal is met.
- Hard cap: maximum iterations or token budget per task.
Practitioners treat this like an API budget: define a hard ceiling per loop, apply exponential backoff on retries, and watch cost per accepted change, not loops run.tecadrise+1
When Loops Are Worth It (And When They’re a Trap)
Loop engineering is real work. Most tasks do not justify it yet. The teams that run agents in production use a simple four-part test before investing in a loop:
- The task repeats at least weekly. One-off jobs are still better handled by a strong manual prompt.
- There is an automatic rejection mechanism (tests, type checks, linters, rules) so bad work fails without human review.gleecus+1
- The agent can do the work end-to-end without handing half back to a human mid-loop.
- “Done” is objective, not a taste call. If quality is subjective, a human still beats a loop at evaluation.
Miss any of these, and you probably want a prompt, not a loop. The cost of a loop compounds and the value collapses if you still need to read and veto most outputs.tecadrise+1
Coding Loops: Claude, GPT, and Production Workflows
Why code was first
Software is the perfect playground for loops because verification is brutally simple: tests pass or fail. There’s no arguing with a red test suite. That makes code an ideal target for “run-until-green” loops.dailydoseofds+1
A typical coding loop looks like this:
- Goal: “All tests in /tests/auth pass, lint is clean, no type errors.”
- Each iteration:Run tests and linters, inspect failures.Choose the highest-impact failure.Apply the minimal code change.Rerun the suite.
- Run tests and linters, inspect failures.
- Choose the highest-impact failure.
- Apply the minimal code change.
- Rerun the suite.
- Verify: all tests green and static checks clean.
- Stop: success or N iterations, then summarize remaining issues.
Claude Code’s /goal and non-interactive -p mode are built around exactly this idea: set a completion condition and let an evaluator-driven loop grind until it’s met.reddit+1
The five building blocks of a serious loop
Modern loop-oriented dev stacks (Claude agents, LangChain-style orchestrators, in-house frameworks) converge on five building blocks:cogitx+1
- Automation – something that keeps the loop running without you (cron, GitHub Actions, /goal sessions, background workers).
- Skills – reusable instruction sets or tools the loop can call by name instead of pasting prompts every time.
- Sub‑agents – separate “maker” and “checker” agents so the writer doesn’t grade its own homework. Writer uses a cheaper, faster model; checker uses a stricter, sometimes larger one.gleecus+1
- Connectors – integrations that let the loop act (open PRs, post messages, update tickets) instead of just suggesting.
- Verifier – the gate (tests, builds, evaluators) that decides if work is accepted.
Stack these and you get fleets of agents working in parallel—rewriting codebases, fixing systemic lint violations, or grinding through migration tasks that would have taken humans months.gleecus
The cost curve nobody loves to talk about
Real loops are not cheap. Each iteration resends accumulated context, and each additional agent that reads that context doubles or triples the bill. A medium-sized coding loop can easily reach tens of thousands of tokens per turn.tecadrise+1
The metric that matters is cost per accepted change: if you accept only a small fraction of results, you’ve just outsourced generation while keeping the expensive review step in-house. Practical teams put hard caps on iterations, token budgets per run, and monitor loop behavior with observability tools like LangSmith or Datadog.tecadrise+1
The Build Order That Works (Any LLM)
You don’t need Claude Code or a custom agent orchestrator to feel how loops behave. You can emulate a simple loop inside any LLM chat, including GPT or Claude, with a single meta-prompt.
Here’s a generic pattern you can paste into your tool of choice:
textYou will work in a loop until the task meets the bar.
TASK:
[describe exactly what you want produced]
SUCCESS CRITERIA (be strict, no soft passes):
- [criterion 1]
- [criterion 2]
- [criterion 3]
LOOP PROTOCOL, repeat every turn:
1. PLAN - state the single next step.
2. DO - produce or improve the work.
3. VERIFY - score the result 1-10 on each criterion.
Be brutally honest. List exactly what is still weak.
4. DECIDE - if every criterion is 8+, print "FINAL" and stop.
Otherwise print "ITERATING" and go again, fixing
the weakest point first.
RULES:
- Never call it done until every criterion is 8 or higher.
- Each pass must fix the weakest score from the last VERIFY.
- Do not ask me questions. Make a sensible assumption, note it,
and keep going.
Begin. Run the loop until FINAL.This forces the model to plan, act, grade its own work against your rubric, and iterate on the weakest dimension until it clears a high bar. You’ve just built a manual loop: the agent is still inside a chat, but the interaction pattern is loop-shaped.
To turn this into a production loop, the build order that tends to work is:
- Get one manual run reliable.
- Extract the instructions into a reusable “skill” or tool.
- Wrap the skill in loop logic (verify + stop conditions).
- Only then schedule it (cron, /goal, background workers).winder+1
Skipping directly to “schedule” is how you end up with runaway loops failing quietly while spending money.
Mira: Loops for Normal Life Inside Telegram
Heavy agent loops make sense for teams that have infra, budgets, and strict gates. Most people just want tasks to run themselves in the background. That’s the niche Mira targets: a messenger-native AI agent built directly into Telegram.binance+1
Mira acts, not just answers. Instead of “write an email draft,” you say “send this email;” instead of “create a ticket,” you get a real ticket in tools like Linear, GitHub, or Notion. It connects to hundreds of apps via Composio and maintains long-term memory across chats and groups, but exposes all of this as plain-language “Skills” you define inside Telegram.efficientlyconnected+1
Everyday loop examples with Mira
Because Mira runs in the same place you already talk to coworkers and friends, loops feel like normal chat messages:binance+1
- “Every weekday at 7am, check my Gmail and Google Calendar and send a 120-word brief of my three most important meetings and urgent emails.”
- “When I forward a message here, turn it into a Linear ticket with the right priority and assign the owner.”
- “Every Friday at 4pm, collect team task status and metrics and post a weekly digest in this chat.”
- “Summarize everything I missed in this group chat while I was away in five bullets.”
The same pattern extends to content creation (voice note → multi-platform posts, image → short video), voice (transcription, summaries, text-to-speech), and life logistics (habit check-ins, journaling, calorie estimates from photos, flight price monitoring).efficientlyconnected+1
Conclusion: Start with Loops You Don’t Have to Build
Loops are not hype; they are the pattern shift from “smart tool you push” to “autonomous worker that runs until done.” Coders feel this first with Claude Code’s /goal loops and GPT-based agents grinding through tests and migrations. Non-engineers feel it through messenger-native agents like Mira that quietly run Skills in the background of their actual lives.binance+3
That said, loops are easy to misuse. Heavy, infra-backed loops makes sense only when tasks are repeatable, verifiable, and objectively “done.” Otherwise, manual prompts plus a disciplined rubric get you most of the value at a fraction of the cost.
My practical take:
- As a developer, start by turning one reliable manual workflow into a loop, with strict verification and a token budget.
- As an everyday user, start with something like Mira and describe one daily Skill you wish would run itself.
Once you’ve felt what it’s like for AI to keep working after you close the tab, it becomes obvious where loops belong—and where you’re better off staying with simple, well-crafted prompts.