Observability-Driven Debug AI: Turn Traces and Telemetry into Reliable Root Cause

Debugging distributed systems with AI can feel like trying to fix a watch while it’s running. Services fail in complex, irreproducible ways; by the time a human developer looks, the context is gone. Large language models (LLMs) are promising because they synthesize patterns across codebases and logs, but left alone they hallucinate causes, propose risky patches, and struggle to reproduce bugs.

There’s a more reliable path: drive the AI with your observability. If you wire OpenTelemetry, structured logs, and time-travel snapshots into a trace-first workflow, you can turn production symptoms into deterministic reproductions. From there, your debug AI can localize root cause, propose constrained patches, and validate the fix in CI using the same traces—cutting hallucinations and avoiding fragile guesswork.

This article is a practical playbook to build that stack. You’ll learn how to:

• Design telemetry so traces become “tests in disguise.”
• Capture inputs, states, and invariants that enable deterministic replay.
• Convert traces into reproducible scenarios and CI gates.
• Guide an LLM to localize faults and propose safe patches.
• Use trace diffs and property checks to validate fixes and prevent regressions.

We’ll cover concrete patterns, code snippets, and safe defaults for OpenTelemetry (OTel), log correlation, and snapshot/record–replay across polyglot microservices.

1) Principles: Why Observability Must Drive the AI

Without grounding, AI is prone to speculation. Observability provides the grounding:

• Causality over correlation: Distributed traces encode causal structure (parent/child, links), making it easier to identify where a fault propagates.
• Deterministic inputs: Capturing inbound requests, message offsets, and seeds enables replay, removing “it works on my machine.”
• Measurable confidence: Comparing traces before and after a fix creates empirical gates (latency, error rates, outliers) instead of subjective code review alone.
• Guarded autonomy: With telemetry-aware tools, an LLM can reason within constraints (e.g., propose only config or code changes that pass replay and trace-based assertions).

A trace-first approach shrinks the distance from a production incident to a unit or integration test you can run in CI. That is the foundation of trustworthy AI-assisted debugging.

2) Architecture Overview

A reference architecture for observability-driven debug AI has four layers:

• Signal layer: OpenTelemetry traces/metrics/logs, structured logs, event metadata, and invariants embedded in telemetry.
• Ground-truth layer: Time-travel snapshots and record/replay: database snapshots, Kafka offsets, HTTP bodies, random seeds, env config, container images.
• Reasoning layer: An orchestration agent that transforms a trace into a reproduction harness, localizes root cause, and proposes minimal patches.
• Validation layer: CI that replays the captured scenario, compares trace diffs, and enforces property/invariant checks.

Data flows from production (signal + ground-truth capture) into a reproducible environment where AI can operate safely and deterministically.

3) Instrumentation: Make Traces Reproducible

Most organizations enable tracing but stop at latency charts. To fuel a debug AI, traces need enough context to recreate the scenario.

3.1 OpenTelemetry essentials

• Propagate W3C Trace Context headers (traceparent, tracestate) across all HTTP/gRPC boundaries.
• Enrich spans with semantic attributes (http., db., messaging., net.) following OTel conventions.
• Use span links for async fan-out/fan-in and message processing.
• Correlate logs to traces using trace_id and span_id in the logging MDC/Context.

Example: Go service with OTel HTTP client/server and log correlation.

go
// go.mod
go 1.21
require (
  go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.51.0
  go.opentelemetry.io/otel v1.26.0
  go.opentelemetry.io/otel/sdk v1.26.0
  go.uber.org/zap v1.27.0
)

// main.go
package main

import (
  "context"
  "log"
  "net/http"

  "go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp"
  "go.opentelemetry.io/otel"
  "go.opentelemetry.io/otel/attribute"
  "go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc"
  "go.opentelemetry.io/otel/sdk/resource"
  "go.opentelemetry.io/otel/sdk/trace"
  semconv "go.opentelemetry.io/otel/semconv/v1.21.0"
  "go.uber.org/zap"
)

func initTracer(ctx context.Context) func(context.Context) error {
  exp, err := otlptracegrpc.New(ctx)
  if err != nil { log.Fatalf("otlp exporter: %v", err) }
  res, _ := resource.Merge(resource.Default(), resource.NewWithAttributes(
    semconv.SchemaURL,
    semconv.ServiceName("payments-api"),
    attribute.String("service.version", "1.4.3"),
  ))
  tp := trace.NewTracerProvider(trace.WithBatcher(exp), trace.WithResource(res))
  otel.SetTracerProvider(tp)
  return tp.Shutdown
}

func main() {
  ctx := context.Background()
  shutdown := initTracer(ctx)
  defer shutdown(ctx)

  logger, _ := zap.NewProduction()
  defer logger.Sync()

  handler := func(w http.ResponseWriter, r *http.Request) {
    ctx := r.Context()
    span := trace.SpanFromContext(ctx)
    // Example: attach replay-critical attributes
    span.SetAttributes(
      attribute.String("user.id", r.Header.Get("X-User-Id")),
      attribute.String("request.id", r.Header.Get("X-Request-Id")),
    )
    logger.With(
      zap.String("trace_id", span.SpanContext().TraceID().String()),
      zap.String("span_id", span.SpanContext().SpanID().String()),
    ).Info("processing payment")

    w.WriteHeader(http.StatusOK)
    w.Write([]byte("ok"))
  }

  http.Handle("/pay", otelhttp.NewHandler(http.HandlerFunc(handler), "POST /pay"))
  http.ListenAndServe(":8080", nil)
}

Key points:

• The logger writes trace_id/span_id so logs can be pulled by trace. Do the same in Java (MDC), Python (structlog/logging Filters), and Node (pino/winston).
• Use semantic attributes to capture fields you’ll need for reproduction (request IDs, tenant IDs, feature flags, seeds, version hashes).

3.2 Messaging and async correlations

If you use Kafka, SQS, NATS, or Pub/Sub, include:

• Message key, topic/queue, partition, and offset (messaging.* attributes).
• Span links to the producing span if available (propagate trace context in headers/headers map).
• Clock skews happen; rely on causal links, not timestamps alone.

Example: Python consumer linking to producer span.

python
# requirements: opentelemetry-sdk, opentelemetry-exporter-otlp, opentelemetry-instrumentation
from opentelemetry import trace
from opentelemetry.trace import Link

tracer = trace.get_tracer(__name__)

def handle_message(msg):  # msg carries headers with trace context
    parent_ctx = extract_otel_context_from_headers(msg.headers)
    links = []
    if parent_ctx:
        links.append(Link(trace.get_current_span(parent_ctx).get_span_context()))
    with tracer.start_as_current_span("process_invoice", links=links) as span:
        span.set_attribute("messaging.system", "kafka")
        span.set_attribute("messaging.destination", msg.topic)
        span.set_attribute("messaging.kafka.partition", msg.partition)
        span.set_attribute("messaging.kafka.offset", msg.offset)
        process(msg.value)

3.3 Log payloads, but keep them structured

Use JSON logs with a stable schema, not free-form text. A minimal structure:

• level, ts, message, service.name, service.version
• trace_id, span_id
• component/module, file:line
• event_type, event_id
• key business fields (user.id, order.id)
• error fields (error.type, error.message, stacktrace)

Structured logs act like rich events to feed the AI with unambiguous context.

4) Time-Travel Snapshots and Record/Replay

For AI-driven debugging to be trustworthy, you need to reconstruct the failing execution. That means capturing:

• Ingress: HTTP/gRPC payloads, headers, auth claims, feature flags.
• Messages: Kafka topic/partition/offset and value bytes.
• Database: a point-in-time snapshot consistent with the trace moment.
• Non-determinism: PRNG seeds, time references (logical clocks), environment variables, config hashes.

4.1 Reconstructable state: define the contract

Your goal is not to snapshot an entire cluster but to capture the minimum set that makes the target service’s behavior deterministic under replay:

• Inputs: exact request bodies, headers, and message bytes.
• State: DB snapshot at a known LSN/GTID/SCN or a logical export that reproduces the same reads.
• Outbound dependencies: optional; you can stub them using contract recordings from the same trace.

4.2 Practical capture strategies

• HTTP/gRPC: enable an ingress capture at the edge (e.g., Envoy Tap, NGINX mirror, or a sidecar) tied to trace_id. Store full payloads securely.
• Kafka: store topic/partition/offset triple plus schema ID (if using Confluent Schema Registry). Fetch exact bytes during replay.
• Databases:
  • Postgres: use pg_basebackup or logical replication snapshot; record WAL LSN for replay; or create a test fixture with COPY of rows referenced by the trace.
  • MySQL: capture GTID set and consistent snapshot with mysqldump --single-transaction.
  • MongoDB: use point-in-time backups or dump specific collections filtered by keys in spans.
• Process state: capture container image digest, build SHA, feature flag snapshot, environment variables.
• System time: allow wall-clock overrides; in code, favor injectable clock interfaces.

For low-level debugging, consider record/replay tools:

• rr (Linux userspace record/replay) for C/C++ processes to step backwards in time.
• CRIU with container runtimes to checkpoint/restore a process state (more advanced; good for long-running reproductions).

4.3 Replay container bundles

Package everything as an artifact your CI and AI agent can pull:

• Container image reference (immutable digest)
• Repro manifest (YAML/JSON): inputs, seeds, DB snapshot location, message offsets, environment
• Trace excerpt (JSON) with spans and enriched attributes
• Sanitized logs linked by trace_id

Example repro manifest:

yaml
repro_version: 1
service: payments-api
service_version: 1.4.3
trace_id: 62b0c1c1b23f8c6c...
container_image: ghcr.io/acme/payments@sha256:abcd...
clock:
  mode: fixed
  epoch_ms: 1718130623123
inputs:
  http:
    - method: POST
      path: /pay
      headers:
        X-User-Id: 42
        Authorization: Bearer <redacted>
      body_ref: s3://debug-bucket/repros/62b0.../pay.json
  kafka:
    - topic: invoices
      partition: 7
      offset: 823423
      value_ref: s3://debug-bucket/repros/62b0.../invoice-823423.bin
state:
  postgres:
    snapshot_ref: s3://snapshots/pg/clusterA/lsn-0/LSN-16/B0/43
  feature_flags:
    enable_new_router: true
non_determinism:
  prng_seed: 123456789
  env:
    REGION: us-east-1

5) Converting a Trace into a Test

Your debug AI shouldn’t brute-force the universe. It should transform a captured trace into a minimal, deterministic test that reproduces the bug.

5.1 Trace-to-test algorithm (outline)

1. Load the trace (JSON) and build the causal DAG. Collapse client spans; keep service spans.
2. Identify failure spans: status != OK, error attributes, abnormal metrics.
3. Walk ancestors to find the minimal input set triggering the failure (delta debugging on inputs if feasible).
4. Extract ingress inputs and state references from span attributes/logs.
5. Generate stubs for downstream calls using data captured in the same trace (e.g., snapshot HTTP responses or SQL query results).
6. Emit a runnable test harness:
   • Spins up the service under test in a container with fixed clock
   • Seeds RNG and environment
   • Applies DB snapshot or fixtures
   • Replays inputs in recorded order
   • Asserts reproduction: same error code/message or same invariant violation

Pseudo-code skeleton:

python
from traces import load_trace, failure_spans, causal_ancestors
from repro import apply_db_snapshot, run_container, replay_http, replay_kafka

trace = load_trace("trace.json")
fail_nodes = failure_spans(trace)
root = causal_ancestors(trace, fail_nodes)

manifest = synthesize_manifest(trace, root)
apply_db_snapshot(manifest.state)

with run_container(manifest.container_image, env=manifest.env, clock=manifest.clock) as svc:
    replay_http(svc, manifest.inputs.http)
    replay_kafka(svc, manifest.inputs.kafka)
    assert svc.exit_code == 0
    assert observed_invariants(svc.logs) == manifest.expected_invariants

5.2 Trace assertions, not just outputs

Under distributed variance, exact outputs might differ (e.g., timestamps), but traces provide robust assertions:

• Error status cleared: span.status = OK where previously ERROR
• Latency improvement: p50/p95 within thresholds for specific spans
• Topology preserved: no unexpected retries/fallback loops
• Invariant checks: absence of specific error logs, presence of compensating transactions

Define a small DSL for trace expectations:

yaml
expectations:
  - span: payments-api POST /pay
    status: ok
    max_ms: 120
  - span: payments-db SELECT charge
    retries: <= 1
  - log_absent:
      level: ERROR
      message: ".*deadlock detected.*"

6) The OpenTelemetry Collector as the Nervous System

Centralize collection and transformation so you can enrich telemetry for replay.

Example Collector config with processors for redaction and attribute extraction:

yaml
receivers:
  otlp:
    protocols:
      http:
      grpc:
  loki:
    # if using Loki log receiver
processors:
  batch: {}
  attributes/replay_enrich:
    actions:
      - action: insert
        key: debug.repro.enabled
        value: true
      - action: upsert
        key: http.request.body_ref
        from_context: http.request.body_ref
  filter/errors:
    traces:
      span:
        - 'attributes["error"] == true or status.code != OK'
  transform/redact:
    error_mode: ignore
    traces:
      - replace_match(attributes["http.request.body"], ".+", "<redacted>")
exporters:
  otlp/grafana:
    endpoint: tempo:4317
    tls:
      insecure: true
  loki:
    endpoint: http://loki:3100/loki/api/v1/push
service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch, transform/redact]
      exporters: [otlp/grafana]
    logs:
      receivers: [otlp]
      processors: [batch]
      exporters: [loki]

Keep redaction processors close to ingestion to reduce risk. Preserve references (body_ref) to fetch full payloads from a secure vault when authorized for debugging.

7) Guiding the AI: Root Cause Localization with Telemetry

With a deterministic reproduction and rich telemetry, the AI can move from guessing to explaining.

7.1 A minimal loop for AI-assisted localization

• Inputs: trace excerpt (spans + attributes), correlated logs, code snippets around error frames, configuration diffs between working and failing versions.
• Task: summarize the failure, identify the most suspicious span(s), hypothesize a localized cause (e.g., null handling, timeout budget, schema mismatch), propose targeted experiments.
• Tools: repository search, blame/annotate, schema registry lookup, feature flag service query.

Prompt skeleton for the agent:

• You can only propose changes to these files or configs.
• You must produce a hypothesis map: {span -> cause -> check}.
• For each hypothesis, generate a micro-test or trace assertion and run it.
• If an edit is proposed, provide a minimal diff with rationale tied to the trace attributes.

Grounding the LLM in trace facts and restricting tool permissions reduces hallucinations.

7.2 Statistical cues from traces (lightweight, effective)

• Delta latency: find spans where latency increased significantly between baseline and failing runs, normalized by workload.
• Error fan-out: count number of child spans that flipped from OK to ERROR; high fan-out parents are often root causes.
• Rare attribute values: outlier analysis on attributes (e.g., region=eu-central-3 seen only in failing traces).
• Retry storms: elevated retry counts or circuit breaker open events near the failure window.

The agent can compute these cheaply and use them to prioritize investigation.

8) Patch Proposals that Don’t Hallucinate

LLMs can draft diffs, but they must be bounded and validated:

• Restrict scope: Only files in modules that own the failing spans; only configuration changes affecting budgets/timeouts; prefer non-functional refactors off.
• Enforce invariants: For database changes, require migrations/tests; for API contract changes, auto-generate consumer tests from trace contracts.
• Require reproduction pass: A candidate patch must flip failing trace expectations to pass in replay.

Example: tightening a gRPC client deadline derived from trace signals.

diff
--- a/internal/client/orders/client.go
+++ b/internal/client/orders/client.go
@@
- ctx, cancel := context.WithTimeout(ctx, 5*time.Second)
+ // Set deadline using p95 from trace baseline (observed 200-300ms); add 3x safety margin
+ ctx, cancel := context.WithTimeout(ctx, 1*time.Second)
  defer cancel()
  resp, err := c.rpc.GetOrder(ctx, req)
  if err != nil {
      return nil, fmt.Errorf("get order: %w", err)
  }

The agent’s rationale should reference the baseline trace p95 and confirm via replay that timeouts no longer trigger retries.

9) CI: Replay, Diff, and Gate

You need fast, deterministic gates that run on every patch (human or AI).

9.1 CI blueprint

• Build immutable images for services touched by the patch.
• Restore DB snapshot or provision a fixture database.
• Start the service under test with a fixed clock.
• Reproduce the trace-driven scenario (HTTP and messages).
• Collect fresh traces/logs.
• Compute trace diffs against the failing baseline.
• Enforce expectations.

Example GitHub Actions job:

yaml
name: Trace Replay Gate
on: [pull_request]

jobs:
  replay:
    runs-on: ubuntu-latest
    services:
      postgres:
        image: postgres:15
        env:
          POSTGRES_PASSWORD: pass
        ports: ["5432:5432"]
    steps:
      - uses: actions/checkout@v4
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Build payments image
        run: docker build -t payments:${{ github.sha }} services/payments
      - name: Restore DB snapshot
        run: psql -h localhost -U postgres -f .repro/pg_snapshot.sql
      - name: Run replay
        run: |
          docker run --rm --network host \
            -e FIXED_CLOCK_MS=1718130623123 \
            -e FEATURE_FLAGS=enable_new_router=true \
            -v $PWD/.repro:/repro \
            payments:${{ github.sha }} \
            /repro/run.sh
      - name: Collect fresh traces
        run: ./tools/pull-traces.sh > new-trace.json
      - name: Compare and enforce expectations
        run: python tools/trace_diff.py baseline-trace.json new-trace.json expectations.yaml

9.2 Trace diff strategies

• Path-wise: compare critical spans by name + attributes.
• Metric-wise: check status, latency, retry count, and error annotations.
• Topology-wise: ensure no new services are called (guard against unintended dependencies).

Keep diffs robust to benign noise by focusing on semantically significant fields.

10) Worked Example: The Case of the Phantom Timeout

Scenario: A payments API occasionally returns 504. Traces show the call fan-outs to an Orders service and a Billing service. Logs include retries and eventual deadline exceeded.

Observability facts:

• payments-api POST /pay latency p95 jumped from 180ms to 2.2s
• orders.GetOrder span p95 stable at 120ms
• billing.Charge span exhibits bimodal latencies: 80ms and 1.9s
• Error logs show circuit breaker opens after 2 retries; feature flag enable_new_router true

Trace-to-test steps:

1. Capture the failing trace and inputs (POST /pay body, headers).
2. Record billing.Charge request/response pairs; store DB snapshot for billing.
3. Generate a replay harness that stubs Orders with recorded responses, but exercises Billing for real.
4. Reproduce 504 deterministically under fixed clock.

AI reasoning:

• Hypothesis A: New router path in billing routes some tenants to an instance with degraded latency due to DNS misconfig.
• Hypothesis B: Deadline budget mismatch: payments has 2s total timeout; billing can take up to 1.9s and has 2 retries with jitter.

Experiments:

• Diff configs: feature flag enabled in failing trace; baseline had it disabled. Replay with flag off; latency returns to normal → supports A.
• Check DNS: not reproduced in replay due to stubs → inconclusive.
• Analyze budgets: with flag on, billing adds a 1.5s backoff before retry under specific error codes → config bug. The trace shows those codes.

Patch:

• Update billing retry policy to cap total attempt time at 600ms when called from payments path (derived from baseline p95 and SLO budget), and propagate a deadline via gRPC metadata so billing aligns.

Validation:

• Replay again; 504 disappears; billing.Charge spans p95 140ms; no circuit breaker opens; expectations pass.
• Rollout with canary; compare production traces; keep automated rollback if expectations regress.

Outcome: Root cause localized to a retry policy interacting with a feature-flagged router. The telemetry made the relationship obvious; the AI just needed to follow the evidence and validate via replay.

11) Safety, Privacy, and Compliance

You will store sensitive inputs and logs to reproduce bugs. Protect them:

• Redaction at source: strip PII at the collector using processors; tokenize identifiers.
• Vaulted payloads: store full inputs in a secure bucket; keep only references in traces/logs.
• Access controls: time-bounded, audited access for the debug AI/agents to fetch payloads; per-incident escrow approvals.
• Data retention: rotate repro artifacts with legal/compliance policies.
• Isolation: run replays in isolated projects/namespaces with network egress restrictions.

For LLM usage:

• No external training: disable model training on your data; use API modes that do not retain content.
• Prompt filtering: strip secrets before sending to the model; keep binary payloads local.
• Tool gating: require human approval for changes that impact schemas, auth, or data retention.

12) Anti-Hallucination Tactics for Debug AI

• Retrieval-first: always attach the relevant trace excerpt, logs, and config diffs to the prompt; avoid “open world” questions.
• Force structured thinking: ask for hypotheses with evidence and tests; reject answers that don’t cite trace attributes or log lines.
• Permissions-as-code: the agent can only change certain files; enforce via repository policy and CI.
• Counterfactual checks: require the agent to explain why other plausible causes are unlikely given the evidence (ablation thinking).
• Reproduce-or-reject: any patch without a passing replay is discarded.

These rules cut speculative patches and encourage evidence-based fixes.

13) Rollout Plan: 30/60/90 Days

• Days 1–30: Baseline instrumentation and correlation
  • Turn on OTel tracing and log correlation for 2–3 critical services.
  • Adopt JSON logs; include trace_id/span_id.
  • Build a minimal Collector pipeline; redact PII.
  • Capture ingress payloads for error traces (trace_id-filtered).
• Days 31–60: Replay and trace-to-test
  • Implement reproducible bundles (manifest + payloads + DB snapshots).
  • Write a runner that starts a service with a fixed clock and replays inputs.
  • Add trace expectations for top 5 incident types.
• Days 61–90: Agent and CI integration
  • Introduce an LLM agent with read-only repo/search and issue summarization.
  • Allow patch proposals in a sandbox branch; gate with replay and trace diff.
  • Add canary monitors that compare live traces post-merge.

Aim for vertical wins: one service, end-to-end, from trace capture to replay and AI patch. Expand laterally once the pattern is solid.

14) Common Pitfalls and How to Avoid Them

• Partial context propagation: one rogue library drops trace headers. Fix by adding middleware at every boundary; write a CI test that fails if traceparent is missing.
• Missing payloads: logs and traces reference inputs that aren’t stored. Install an edge tap or middleware to capture payloads behind a feature flag.
• Non-deterministic clocks: code that calls time.Now() in multiple places—wrap with an injectable clock interface.
• Snapshot drift: DB snapshots don’t align with message offsets. Record a transactional marker (e.g., write trace_id to DB within the same transaction) to bind them.
• Over-broad redaction: redacting everything makes reproduction impossible. Use field-level policies and vault payloads with references.
• Agent overreach: unconstrained code edits. Lock down directories and require trace-linked rationale for changes.

15) Tooling and Ecosystem Notes

• Tracing backends: Jaeger, Zipkin, Grafana Tempo, Honeycomb; all accept OTel.
• Logs: Loki, OpenSearch, Elasticsearch, or ClickHouse-backed stores; ensure trace correlation fields exist.
• Metrics: Prometheus with exemplars tied to traces can be useful for selecting interesting traces.
• Snapshot tech: CRIU for process snapshots, rr for C/C++ record/replay, Testcontainers for on-demand DB fixtures.
• Property-based testing: Hypothesis (Python), jqwik (JVM), fast-check (TypeScript) to explore input spaces around the captured seed.
• Message schemas: Confluent Schema Registry or Protobuf descriptors; store schema IDs in span attributes.

16) Measuring Success

Track these KPIs to know the system is working:

• Reproducibility rate: percentage of P1/P2 incidents that can be deterministically replayed within 24 hours.
• Patch acceptance rate: fraction of AI-proposed patches merged after passing gates.
• Regression rate: percentage of AI patches that trigger rollbacks within 7 days (aim for lower than human baseline).
• MTTR reduction: time from alert to merged fix; attribute to trace-to-test automation.
• False hypothesis rate: proportion of AI-localized causes invalidated by replay.

Use a before/after study over 4–8 weeks on a small slice of services.

17) References and Further Reading

• OpenTelemetry Specification and Semantic Conventions (opentelemetry.io)
• W3C Trace Context (www.w3.org/TR/trace-context/)
• Dapper: A Large-Scale Distributed Systems Tracing Infrastructure (Google)
• rr: Practical record and replay for Linux (rr-project.org)
• CRIU: Checkpoint/Restore In Userspace (criu.org)
• Jepsen: On the correctness of distributed systems (jepsen.io)

Closing Thoughts

Observability-driven debug AI is not about sprinkling AI on top of logs; it’s about turning your traces into executable truth. When you capture the right inputs and state, you make every failure a reproducible test. With that foundation, an AI agent can help localize faults, craft minimal patches, and prove they work—using the same traces that captured the bug in the first place.

The payoff is profound: fewer speculative fixes, faster incident recovery, and a growing corpus of trace-as-test artifacts that double as living documentation of your system’s real behavior. Start with one service, wire the path from trace to replay, and let the AI operate where it’s strongest—reasoning over concrete evidence.