Our production webhooks stopped working on a Wednesday afternoon. Every single webhook call returned the same error: “error occurred in generating the token details.” The system had been running stable for weeks, and I had one error log, zero context on what changed.
I pasted the raw error log into my coding agent and asked it to find the root cause. AI Architect, which already runs as part of our daily workflow, provided deep codebase context across all our repositories and enabled the coding agent to trace the failure to a completely different service from where the error surfaced.
Within 10 minutes, I had a full root cause analysis, an immediate database fix, and a permanent code patch. Total cost: $0.91.
The problem
The error pointed to a token generation failure inside one of many microservices in our backend. The log contained integration details, agent references, and workspace metadata, but nothing that explained why token generation suddenly broke after weeks of stable operation.
Cross service issues like this one typically require jumping between multiple repositories, reconstructing call chains manually, and pulling in engineers familiar with the specific pipeline. That process regularly takes hours and involves multiple people.
What I did with AI Architect
I enabled AI Architect’s MCP in my coding agent, pasted the error log, and gave one instruction: “find the root cause.”
I gave no hints about which service to investigate, no pointers to suspicious functions, and no context on what changed recently.
AI Architect had already indexed all our repositories and built a knowledge graph of how our services, configurations, and code paths connect to each other. This deep codebase context gave the coding agent the ability to search across repos, read specific file ranges, and reason over cross service dependencies from the very first query.
Step 1: Locating the error source (2 minutes)
The coding agent called AI Architect’s listRepositories tool to orient itself across the full repository landscape. It then searched for the error string across our codebase, located the exact function in the webhook handler service where the token extraction fails, and identified that the actual failure originates deeper in the call chain than where the error surfaces.
Step 2: Tracing the code flow across services (3 minutes)
Using AI Architect’s searchCode and getCode tools, the coding agent pulled the full implementation of the token extraction function. Here is the simplified version of what it found:
func ExtractTokens(config Configuration) (*TokenData, error) {
// Decrypt authentication tokens
accessKey, err := DecryptData(config.EncryptedAccessKey)
// Extract provider configuration
providerType := GetProviderType(config)
if providerType == "" {
return nil, errors.New("invalid provider type")
}
return &TokenData{AccessKey: accessKey, Provider: providerType}, nil
}
The GetProviderType function reads a provider configuration field from the agent config. When that field is missing, it returns an empty string, which triggers the “invalid provider type” error downstream:
func GetProviderType(config Configuration) string {
provider := config.GitProvider
if provider == nil {
return ""
}
return provider.Type
}
The agent now had its hypothesis: the provider configuration field was missing entirely. The real question was why.
AI Architect’s cross repo context allowed the agent to immediately trace this dependency into the configuration service in a separate repository, without any manual guidance from me on where to look.
Step 3: Identifying the root cause (4 minutes)
The coding agent found the function responsible for merging old and new configuration values during API updates. This function explicitly preserves certain sensitive fields from the existing database record, but the provider configuration field was never included:
func UpdateConfig(newConfig Configuration) error {
existingConfig := GetExistingConfig()
newConfig.EncryptedAccessKey = existingConfig.EncryptedAccessKey
newConfig.EncryptedGitToken = existingConfig.EncryptedGitToken
// Bug: GitProvider field not preserved
return SaveConfig(newConfig)
}
The agent then inspected the validation layer. The validator checks for the provider field, but when it is missing, it logs a warning and returns an empty string. Validation passes, and the broken configuration gets saved to the database without raising an error.
Root cause confirmed: an API update to the agent configuration silently dropped the provider configuration field because it was never included in the field preservation logic.
Every subsequent webhook call failed because the token extraction flow could no longer determine which provider to authenticate against. The validation layer masked the issue by treating a missing critical field as a non-blocking warning.
Step 4: Producing the fix (1 minute)
The coding agent generated a permanent code fix that adds the missing field to the preservation logic:
func UpdateConfig(newConfig Configuration) error {
existingConfig := GetExistingConfig()
newConfig.EncryptedAccessKey = existingConfig.EncryptedAccessKey
newConfig.EncryptedGitToken = existingConfig.EncryptedGitToken
newConfig.GitProvider = existingConfig.GitProvider // Added
return SaveConfig(newConfig)
}
It also generated an immediate database patch to restore the missing field for the affected workspace. Both fixes were scoped directly to the root cause and ready to apply.
The outcome
| Metric | Value |
| Time to root cause | Under 10 minutes |
| AI cost | $0.91 |
| Repositories searched | 50+ |
| Services traced | 3 |
| Input provided | One error log line, zero additional context |
| Deliverables | Database fix, code patch, full RCA document |
The bug spanned three layers: a configuration update API in one service, the database persistence layer, and the token extraction logic in a completely different service.
AI Architect’s knowledge graph gave the coding agent structured context over how these services, functions, and configuration schemas relate to each other, and the agent reasoned over actual repository structure and cross service dependencies rather than relying on unguided grep searches and manual file exploration.
For a detailed breakdown of the root cause analysis that AI Architect produced, see the [full RCA document].
What this means for complex debugging
This is the same class of problem where AI Architect delivers the strongest gains on benchmarks: multi file, cross service tasks where architectural understanding determines whether the agent succeeds or fails. [Learn more here]
We will keep publishing real examples from our own codebase, because the most convincing proof of a developer tool is the team behind it using it every day.
