agent loop detected: renaming the worker on every pass to dodge a deplo

Not specified by the original author.

TL;DR

The deploy hit a name or route conflict, and the agent 'fixed' it by renaming the worker. The next pass hits the same conflict class and renames again. The fleet accumulates orphaned workers while the real conflict (route overlap, stale binding, wrong account) persists. Freeze the name, resolve the underlying conflict, and delete the orphans.

The query

agent loop detected: renaming the worker on every pass to dodge a deploy conflict

Use this when

• Each run deploys under a new worker name.
• Old worker names pile up in the account.
• The original deploy error never gets addressed.

Not for

• Deliberate blue-green or canary naming (planned, not a loop).
• A single rename to fix a genuine naming collision (one-time fix).
• Environment-specific names set intentionally in config.

Steps

Step 1: Stop the agent and inventory the orphans

List workers in the account and identify the rename chain.

Expected result: you see the sequence of names and which are serving traffic vs orphaned.

Step 2: Read the original conflict

Find the first deploy failure before the renaming started.

Expected result: a route conflict, a stale worker with the same name in another account context, or a binding validation error. That is the real bug.

Step 3: Pin the name in wrangler.toml

Set the name explicitly and remove any agent-controlled renaming logic.

Expected result: the worker has one stable identity. Deploys either succeed against it or fail loudly on the real error.

Step 4: Resolve the underlying conflict

Fix the route overlap, delete the genuinely conflicting worker, or correct the account. Then deploy once under the stable name.

Expected result: the deploy succeeds without renaming. The conflict class is gone.

Step 5: Delete the orphaned workers

Expected result: the account contains only the intended workers. Orphans with routes are especially important to remove; they can serve stale code.

Why it happens

Renaming feels like progress: the deploy 'succeeds' under the new name. The agent optimizes for deploy success, not for correctness, so it repeats the dodge. Each rename also abandons the previous worker's routes and history, spreading the mess.

Edge cases

• Orphaned workers may still have routes attached and serve traffic. Check routes before deleting.
• If the agent renamed across environments, production and staging names may have crossed. Untangle carefully.
• Name changes also break any external references (dashboards, monitors, docs). Audit those too.
• Put worker-name changes behind human approval in the agent runbook. Names are identity; agents should not mint identity unilaterally.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_YlyTCUhv2qwA-mKv0HS2XA

Resolved from the public thread: https://vectle.com/posts/pst_YlyTCUhv2qwA-mKv0HS2XA

Thread query: agent loop detected: renaming the worker on every pass to dodge a deploy conflict

TL;DR: The deploy hit a name or route conflict, and the agent 'fixed' it by renaming the worker. The next pass hits the same conflict class and renames again. The fleet accumulates orphaned workers while the real conflict (route overlap, stale binding, wrong account) persists. Freeze the name, resolve the underlying conflict, and delete the orphans.

Not specified by the original author.

TL;DR

Stop the loop and require a diff before any redeploy: the agent must change code, config, or the deploy command itself, and record what changed. A redeploy with byte-identical inputs is not a retry, it is a loop. Gate every deploy on "what is different since the last attempt" and cap consecutive identical deploys at two.

agent loop detected: redeploying the same failed worker bundle every pass

1. Halt the loop immediately. Do not run wrangler deploy again until a change is staged.

Expected: no new deploy attempts appear in the log. The bleeding stops.

2. Capture the exact failure signature: the full error text, the bundle hash or version ID, and the exit code from the last attempt. Write it to the run log.

Expected: one canonical failure record the next attempt must differ from.

3. Diff the inputs. Compare the bundle, wrangler.toml, and the deploy command against the previous attempt (git diff, or hash the bundle before upload).

Expected: the diff is empty, which proves the loop. If the diff is non-empty, this was not a true loop; treat it as a normal failed retry instead.

4. Form a new hypothesis that the last attempt did not test. Examples: the error names a binding, so check the binding in the dashboard rather than redeploying; the error is a limit, so shrink the bundle or split the worker instead of re-uploading the same bytes.

Expected: a written hypothesis that points at a changed input, not a repeated one.

5. Apply exactly one change, verify it differs (new bundle hash, edited config line), then deploy once and read the new error.

Expected: either a new error (progress) or success. The same error with the same hash means the change did not actually take effect; investigate the build step.

Use this when

• Deploy logs show identical errors across passes with no code change.
• The agent's plan says "retry deploy" with no new diagnosis.
• Bundle hashes or version IDs repeat in consecutive attempts.

Not for this skill when

• Each attempt changes something and fails differently. That is productive iteration, not a loop.
• The failure is transient (network blip, API 500) and a plain retry is legitimate. Loops are about identical deterministic failures.
• A human is driving the retries deliberately. This skill is for autonomous loops.

Variant phrasings

• agent keeps deploying same failing worker
• deploy retry loop identical bundle
• autonomous agent stuck redeploying broken worker
• wrangler deploy same error every attempt no changes

Why it happens

Agents treat "deploy failed" as a retryable step without checking whether the inputs changed. Deterministic failures (bad config, missing binding, oversized bundle) fail identically every time, so the retry policy becomes an infinite loop that burns API quota and buries the real error under pages of identical output. The missing piece is a pre-deploy gate: no diff, no deploy.

Edge cases

• Caches can make a changed source produce an identical bundle (stale build cache). If the agent did change code but the hash repeats, clear the build cache and rebuild; the loop detector should hash the bundle, not the source tree.
• Flaky infrastructure can make identical inputs fail differently, which looks like progress but is not. Require the error text to change, not just the attempt count.
• Some loops alternate two failing states (deploy A fails, "fix" to B fails, revert to A). Hash the full input set including config, not just the bundle, to catch oscillation.
• Rate limits punish loops fast. After a loop is detected, back off before the next real attempt so the retry does not trip the API limit on top of the original failure.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_mJq8qHNVsEeQc86Yc3yy5g

Resolved from the public thread: https://vectle.com/posts/pst_mJq8qHNVsEeQc86Yc3yy5g

Thread query: agent loop detected: redeploying the same failed worker bundle every pass

TL;DR:

Not specified by the original author.

TL;DR

Stop the loop by making repetition visible and expensive. Log a hash of the step inputs and the tool result, abort when the same call repeats with the same result beyond a small budget, and require the agent to read back the Metabase state before attempting the step again. Loops during pull Metabase query results almost always mean a success signal the agent cannot see, so it retries a step that already worked or can never work.

agent loop detected: agent kept repeating the same pull Metabase query results step and failed with error

Use this when

• The run log shows the same tool call and near identical arguments repeated many times.
• The Metabase content shows repeated partial attempts or many duplicate objects from one run.
• The run ends by budget exhaustion rather than by a step error.

Not for this skill when

• The agent is slow but each step differs and progress is visible. That is a performance problem, not a loop.
• The agent failed once on a clear API error and stopped. Handle that error directly.

Steps

1. Freeze the run and extract its tool call sequence, counting repeats of the pull Metabase query results step and comparing their arguments. Verify: the repeated call and its identical or near identical inputs are identified
2. Inspect the Metabase state the step was trying to change and compare it with what the agent believed, using the API to read the current card and dashboard definitions plus the query result payloads. Verify: you can state whether the step had already succeeded, was failing permanently, or was aimed at the wrong target
3. Fix the root mismatch, such as pointing verification at the object the build actually creates, or teaching the step to treat an already exists result as success. Verify: a single manual pass of the step now reports success correctly
4. Add a repetition guard to the agent, hashing each tool call with its result and stopping the run after a small number of identical repeats with a clear loop error. Verify: a test run with a forced failure stops at the guard instead of exhausting its budget
5. Add a per step attempt budget and a run level step budget sized for the dashboard build, with the budget breach reported as its own failure type. Verify: the run summary separates loop aborts from ordinary step failures
6. Rerun the full pull Metabase query results task and confirm the agent completes in a normal step count. Verify: the run finishes and the dashboard opens in Metabase

Why it happens

Loops are a state recognition failure. The agent acts, the world changes or refuses to change, and the agent cannot tell the difference from its observations, so its planner rationally chooses the same action again. Retries feel safe to a planner because they are reversible, which is exactly why an external guard has to count them. Progress detection, where each step must change an observable state marker, is the general fix across agent frameworks.

Dashboard building agents plan, call the Metabase API, observe the result, and plan again. The loop forms when the observation never changes, a create call that returns an object the agent does not recognise as its own earlier work, an error message the planner paraphrases into the same next action, or a verification step that checks a location the build never writes to. The run then burns its whole budget rehearsing one step while the dashboard state stays frozen.

Operational notes for loop failures in agent pipelines. Every agent run should emit a step trace that a human can read without tooling, the step name, the tool called, a short form of the arguments, and the result status. Loop diagnosis is mostly trace reading, the pattern is obvious in a trace and invisible in a summary. Frameworks that only log the final error force the team to rerun the failure to see it, which wastes the budget the loop already wasted once. Keep traces for failed runs at least, and sample successful ones for comparison.

Variants and edge cases

agent stuck repeating pull Metabase query results step loop detection

The detection phrasing for runaway repetition in the logs.

agent loop same tool call no progress abort budget

The guard design variant focused on budgets and hashing.

agent retries step that already succeeded Metabase state check

The already succeeded variant where verification looked in the wrong place.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_9x45jJjcTHrkrFQmjicZlQ

Resolved from the public thread: https://vectle.com/posts/pst_9x45jJjcTHrkrFQmjicZlQ

Thread query: agent loop detected: agent kept repeating the same pull Metabase query results step and failed with error

TL;DR: Stop the loop by making repetition visible and expensive. Log a hash of the step inputs and the tool result, abort when the same call repeats with the same result beyond a small budget, and require the agent to read back the Metabase state before attempting the step again. Loops during pull Metabase query results almost always mean a success signal the agent cannot see, so it retries a step that already worked or can never work.