Diagnose DeepSeek super agent one boundary at a time
A DeepSeek super agent failure can appear as silence, a repeated task, an unexpected worker, or a reply without a useful result. These symptoms do not identify the failing component on their own. The fastest useful investigation is to locate the last boundary that worked and the first one that did not. That approach avoids reinstalling everything when the actual problem is a missing repository permission or a misunderstood policy mode.
The DeepSeek super agent prototype combines external event sources, a router, task records, worker sessions, local workspaces, and outbound communication. Each layer has its own evidence. An event arriving is different from a task being admitted. A task being admitted is different from a worker finishing. A worker finishing is different from a response being delivered. Keep those statements separate in your troubleshooting notes.
This DeepSeek super agent guide provides a diagnostic method, not a catalogue of observed bugs in every release. We have not executed your installation. Use the upstream repository at your pinned revision to identify exact configuration names, paths, commands, and supported behavior. If source and a tutorial disagree, investigate the source before changing working infrastructure.
When DeepSeek super agent does not start
Begin a DeepSeek super agent startup investigation with the earliest meaningful error. A long stack trace often contains secondary errors caused by the first missing dependency or invalid configuration. Record the command, working directory, runtime version, source revision, and the first error message. Do not paste your entire environment into a public issue; it may contain credentials that have nothing to do with the failure.
DeepSeek super agent relies on the runtime and dependencies expected by its current implementation. Confirm the Node version and available SQLite support against the documented requirements. A package install that completed successfully does not prove that the runtime launching the application is the same one used during installation. Service managers and interactive terminals can have different executable paths and environments.
Next, inspect whether DeepSeek super agent can read its configuration and write its intended state directory as the dedicated OS user. A permissions problem should be fixed at the intended directory, not by broadly elevating the whole process. Once startup succeeds, verify a small next step before changing more settings. That preserves the connection between the original symptom and the change that resolved it.
When DeepSeek super agent cannot see GitHub activity
A silent DeepSeek super agent installation may have a working process but an incorrect GitHub identity. Confirm which account the integration actually uses, then verify that this account can access the configured repository through the expected authentication mechanism. The prototype documents a dedicated user account rather than a GitHub App. A credential that works in another product may therefore be unsuitable for this workflow.
For DeepSeek super agent, distinguish repository visibility from event delivery. Being able to read repository files does not establish that the relevant notifications or events reach the integration. Check the source conversation and the event mechanism described upstream. Use one controlled test signal so you can recognize it in the logs. Sending many similar comments makes the investigation harder and may create several tasks after the problem clears.
If DeepSeek super agent sees activity from the wrong repository, inspect the exact owner/name configuration and the environment used by the running process. Do not assume an edited file has been reloaded. Capture the effective configuration without secrets, and confirm the restart or reload behavior documented for that revision. A stale process can make a correct new configuration look ineffective.
When DeepSeek super agent sees an event but does nothing
An ingested DeepSeek super agent event is not automatically actionable. The system may reject it because of the actor, repository scope, conversation context, or policy. Find the decision associated with the event before treating the absence of a worker as an execution failure. This is where a record connecting source events to task admission becomes valuable.
Ask whether the DeepSeek super agent event is an explicit request or an inferred opportunity. An ordinary discussion may be interesting to a human without being a direct command to the agent. Conversely, a clear authorized instruction can take a path that remains enabled in every inference mode. Misclassifying the request leads to misleading conclusions about off, observe, or auto.
Check the authorized identity mapping for DeepSeek super agent. The name displayed in a conversation may not be the account identifier used by the configuration. Keep the agent’s own account distinct from the human requester. If you need to change authorization, make the smallest intended change and repeat the controlled test. Do not grant broad access to all participants merely to see whether a task will start.
When DeepSeek super agent runs in off or observe mode
DeepSeek super agent mode names are easy to overinterpret. In the published design, off, observe, and auto describe admission of inferred work. They do not universally disable explicit requests. If an authorized person directly requests a task while off or observe is selected, a resulting worker is not by itself evidence that the mode is broken.
To investigate DeepSeek super agent mode behavior, preserve the exact source event and identify its admission path. Test an ambient event separately from a direct request. Compare the resulting decision with the configured mode at the time the event was processed. If the configuration changed during the experiment, record that timing rather than assuming the latest value governed all earlier work.
If your intention is to stop every DeepSeek super agent execution path, use the appropriate process and access controls. Do not rely on an inference mode as an emergency stop. Stopping new admission may also be different from stopping an already running worker. Inspect active state before resuming operation so that older work is not mistaken for a newly admitted request.
When a DeepSeek super agent worker stalls
A DeepSeek super agent worker can be waiting for a model response, a tool, a subprocess, a filesystem operation, or another dependency. Start with the worker’s last visible action. Look for evidence that the action is still progressing, has failed, or is waiting for input. A lack of new text alone is not enough to decide that killing and restarting the entire application is the correct response.
DeepSeek super agent model access should be diagnosed separately from repository access. Check the configured endpoint, credentials, quota, and error response without exposing secrets. A provider error is not necessarily a routing error. Likewise, a command that fails because a repository dependency is absent should be investigated in the worker workspace rather than repaired by changing the event integration.
Before retrying a DeepSeek super agent task, inspect whether it already changed files or performed external actions. Retrying from the original instruction may repeat side effects. Preserve the task record and workspace long enough to understand what happened. Then choose whether to continue the existing task, create a new bounded task, or restore the disposable evaluation environment to a known baseline.
When DeepSeek super agent produces the wrong change
A completed DeepSeek super agent task can still fail its acceptance criteria. Compare the artifact with the original request, the repository context, and the constraints the worker received. Was the request ambiguous? Did the worker operate in the expected checkout? Did it modify files outside the intended scope? These are distinct failure modes that deserve distinct fixes.
Review DeepSeek super agent claims against actual evidence. If a summary says checks passed, identify which checks ran and what they covered. A narrow retry after a transient failure is not equivalent to a full validation run. If the task was a text change, a clear diff may be sufficient; if it changed behavior, meaningful validation should match that behavior. Do not demand unrelated tests merely to make the report longer.
Improve the next DeepSeek super agent request by specifying the output and acceptance boundary, not by adding a huge generic instruction block. A short request with named files, a concrete desired behavior, and a review condition is often easier to evaluate. Keep a failed example in your evaluation notes because it reveals operating requirements that a successful demonstration may hide.
When DeepSeek super agent replies are missing or duplicated
DeepSeek super agent can complete work even if the outbound response fails. Inspect the task state and worker artifact before assuming the original request was ignored. Then check the destination conversation, account permissions, and channel-specific delivery error. This sequence reduces the chance that repeated user requests create duplicate changes while the real problem is an inability to post a reply.
Duplicate DeepSeek super agent work requires a different investigation. Compare the source events and task identities. Determine whether several distinct requests were submitted, one event was processed more than once, or related conversation activity was interpreted as new work. Preserve the timing and original context. Deleting logs or state prematurely can remove the evidence needed to distinguish those possibilities.
With optional Feishu integration, DeepSeek super agent has another identity and delivery boundary to inspect. A working GitHub response does not establish that Feishu credentials, event handling, and conversation permissions are correct. Test each channel with a minimal request and record where the response appears. Avoid using a busy shared conversation as an uncontrolled diagnostic queue.
When DeepSeek super agent state looks inconsistent
DeepSeek super agent state includes durable records and local workspace material. If the panel, task record, and filesystem appear to disagree, document each observation before making changes. Identify the active process and the directories it uses. Two processes launched with different working directories or configurations can create a confusing picture without any single record being intrinsically corrupt.
Do not delete the DeepSeek super agent database as a first troubleshooting step. A reset may erase the connection between requests and work while leaving external changes intact. Back up relevant state in a protected location, review the implementation’s recovery procedure, and decide what you intend to preserve. A disposable test environment makes this decision easier than a machine already serving real collaborators.
For DeepSeek super agent upgrades, compare the previous and current revisions and inspect persistence-related changes. Restoring old code is not guaranteed to restore the meaning of new data. Record a recovery plan before upgrading rather than improvising after a failure. If no supported migration path is documented, treat that uncertainty as part of the prototype evaluation.
Write a useful DeepSeek super agent issue report
A strong DeepSeek super agent issue report states the expected behavior, actual behavior, minimal reproduction, source revision, and relevant environment details. Include the last confirmed successful boundary and the first failing one. This tells maintainers whether to begin with ingestion, policy, execution, state, or delivery. A short, reproducible report is more useful than an unfiltered transcript of an entire machine.
Redact DeepSeek super agent credentials, private repository content, personal information, and unrelated logs before posting publicly. Replace sensitive names consistently so the relationship between actors remains understandable. Explain whether the issue occurs in a disposable repository and whether a restart or configuration change affects it. If you cannot reproduce it consistently, say so directly.
Finally, distinguish your DeepSeek super agent observations from assumptions. “The worker log contains this error” is stronger than “the model is broken.” “The explicit request ran in observe mode” may describe expected behavior. Good diagnostics preserve uncertainty until evidence resolves it. Return to the architecture guide for the lifecycle, or the setup guide to rebuild a controlled evaluation sequence.