AI Debugger¶

The AI Debugger is an automated root-cause analysis engine that examines your entire application — the Project Graph, event timeline, diagnostics, and gap analysis — to identify, cluster, and rank issues, then suggest safe fixes. It is read-only and never modifies your code or database.

Quick Start¶

from aksara.ai.debugger import run_debugger

report = run_debugger()                    # full analysis
report = run_debugger(query="login fails") # focused analysis

print(report.summary)
for rc in report.root_causes:
    print(f"  [{rc.confidence:.0%}] {rc.title}")

Architecture¶

The debugger runs a 6-step pipeline:

┌─────────────┐     ┌────────────┐     ┌──────────┐
│ 1. Load      │────▶│ 2. Extract │────▶│ 3. Filter│
│ Project Graph│     │ Issue Pool │     │ by Query  │
└──────────────┘     └────────────┘     └──────────┘
        │                                     │
        ▼                                     ▼
┌──────────────┐     ┌────────────┐     ┌──────────┐
│ 6. Suggest   │◀────│ 5. Rank    │◀────│ 4.Cluster│
│ Safe Fixes   │     │ Root Causes│     │ & Detect │
└──────────────┘     └────────────┘     └──────────┘

Load Project Graph — builds the full application graph (models, routes, queries, diagnostics, migrations, events).
Extract Issue Pool — converts diagnostics, gap items, and error events into normalised DebugIssue objects.
Filter by Query — if a query string is provided, keeps only issues matching the keywords.
Cluster & Detect — groups issues by shared component (model, route, query) and detects root causes using 6 heuristic patterns.
Rank Root Causes — scores each root cause by severity, evidence count, related clusters, and affected issues. Confidence is normalised to 0–1.
Suggest Safe Fixes — generates actionable, category-specific suggestions (never auto-applies them).

Data Models¶

DebugIssue¶

Field	Type	Description
`id`	`str`	Unique issue identifier
`source`	`str`	Origin: `diagnostic`, `gap`, `event`
`severity`	`str`	`critical`, `error`, `warning`, `info`
`title`	`str`	Short description
`message`	`str`	Detailed explanation
`related_models`	`list[str]`	Affected model names
`related_routes`	`list[str]`	Affected route paths
`related_queries`	`list[str]`	Affected SQL patterns

IssueCluster¶

Field	Type	Description
`cluster_id`	`str`	Unique cluster identifier
`label`	`str`	Human-readable cluster name
`component_type`	`str`	`model`, `route`, or `query`
`component_name`	`str`	Specific component name
`issue_ids`	`list[str]`	Member issue IDs
`severity`	`str`	Worst severity in the cluster

RootCause¶

Field	Type	Description
`cause_id`	`str`	Unique cause identifier
`title`	`str`	Root cause title
`description`	`str`	Detailed explanation
`confidence`	`float`	0.0–1.0 confidence score
`severity`	`str`	Worst severity of related issues
`evidence`	`list[str]`	Supporting evidence strings
`related_issues`	`list[str]`	Contributing issue IDs
`related_clusters`	`list[str]`	Contributing cluster IDs
`category`	`str`	`database`, `migration`, `ai_provider`, `route_errors`, `security`, `general`
`fix_suggestions`	`list[str]`	Safe remediation steps

DebugReport¶

Field	Type	Description
`issues`	`list[DebugIssue]`	All extracted issues
`clusters`	`list[IssueCluster]`	Grouped clusters
`root_causes`	`list[RootCause]`	Ranked root causes
`summary`	`str`	Human-readable summary
`query`	`str \| None`	Original query if provided
`elapsed_ms`	`float`	Pipeline duration in milliseconds
`generated_at`	`str`	ISO 8601 timestamp

Root Cause Detection¶

The debugger uses 6 heuristic patterns to detect root causes:

Category	Detects
`database`	Multiple DB-related issues (connection, query, tracing)
`migration`	Pending or failed migrations
`ai_provider`	AI provider configuration or connectivity issues
`route_errors`	Routes with repeated errors
`security`	Security warnings across models
`general`	Large clusters suggesting systemic problems

Studio Integration¶

API Endpoint¶

POST /studio/ai/debug
Content-Type: application/json

{"query": "login fails"}     # optional

Response:

{
  "ok": true,
  "query": "login fails",
  "issues": [...],
  "clusters": [...],
  "root_causes": [...],
  "summary": "Found 12 issues in 4 clusters with 2 root causes.",
  "counts": {"issues": 12, "clusters": 4, "root_causes": 2},
  "elapsed_ms": 42.3,
  "generated_at": "2026-03-01T12:00:00Z"
}

UI Panel¶

The Studio sidebar includes an AI Debugger panel with:

Query toolbar — type a question and click Run
Summary grid — issue, cluster, and root cause counts at a glance
4 tabs: Root Causes, Clusters, Issues, Fix Plan
Severity colour-coding and confidence badges

Console Integration¶

The AI Console recognises debug-related natural language:

aksara> debug my app
aksara> why is login failing?
aksara> find bugs in User model
aksara> diagnose database issues
aksara> troubleshoot authentication

The intent router maps these to the debug flow, which executes the full debugger pipeline and returns a structured report.

CLI Command¶

# Full analysis
aksara ai debug

# Focused analysis
aksara ai debug --query "login fails"

# JSON output
aksara ai debug --json

# Summary only
aksara ai debug --summary

# Filter by model or route
aksara ai debug --model User
aksara ai debug --route /api/login

# Combine options
aksara ai debug --query "auth" --json --summary

Safety Guarantees¶

The AI Debugger is read-only by design:

Never modifies source code files
Never alters database state
Never reconfigures services
All fix suggestions are informational only
Users must manually apply any recommended changes

Best Practices¶

Start broad, then focus — run without a query first, then drill down with specific queries based on the results.
Check root causes first — they are ranked by confidence; the top cause is most likely the primary issue.
Use the Fix Plan tab — it groups suggestions by root cause in priority order.
Run after changes — re-run the debugger after applying fixes to verify improvement.
Combine with other tools — use the Project Graph to understand relationships and the Event Timeline to see recent changes.