failure-intelligence.md

Failure Intelligence

Failure intelligence is the capability to detect, remember, and act on failures as knowledge, not just telemetry.

What Kakveda adds (layer model)

Most stacks already have:

• LLM runtime (Ollama/OpenAI/etc.)
• tracing/logging (OpenTelemetry, logs)
• evaluation pipelines

Kakveda adds:

• a failure knowledge base
• pre-flight matching (“this failed before”) before executing
• pattern detection (recurrence, trends)
• system health scoring over time

Pre-flight memory check

Before a run executes, Kakveda can:

1. normalize inputs
2. compute a fingerprint
3. match against known failures/patterns
4. apply policy → allow/warn/block/route

Why it matters

• Reduces repeated failures in production
• Gives teams a shared memory of failure modes
• Enables preventive controls (warn/block) and safer routing
• Improves long-term system reliability (health-based management)

Practical examples

• Prompt or tool configuration that repeatedly causes parsing errors
• Tool outages that recur (timeouts, 5xx) — detect pattern and route
• Model regressions between versions — detect rising severity in patterns

Extensibility

Matching can be:

• deterministic (hash/fingerprint)
• rules/heuristics
• embedding similarity (optional extension)

Policy can be:

• warn-only
• warn + confirmation gate
• block high-severity failures
• route to safer model/tools