Automated Experiment Summaries: Use AI Agents to Draft Post-Run Reports (Safely)

Hook an AI agent into your ClickHouse analytics stack to auto-generate safe, reliable run summaries

Hook: You run quantum experiments that produce terabytes of logs, metrics and simulator traces—but your team wastes hours manually summarizing runs, hunting for regressions, and redacting sensitive telemetry before sharing. In 2026, with agents getting file-system and dataset access across platforms, automated, trustworthy run summaries are essential. This blueprint shows how to safely plug an AI agent into a ClickHouse-backed analytics pipeline to produce reproducible, auditable post-run reports while preventing data exfiltration and hallucinations.

Why this matters now (2025–2026 context)

ClickHouse has continued its rapid adoption as an OLAP backbone for high-throughput experiment telemetry—its large funding and platform momentum through late 2025 made it a default choice for analytics-heavy research stacks. At the same time, AI agents gained advanced autonomy (desktop and file access in late 2025), increasing the risk of accidental data exposure when agents are granted dataset access. That combination creates both opportunity and risk: agents can synthesize run-level insights automatically, but without strict guardrails they can leak secrets or generate plausible-but-false conclusions.

Takeaway: Use a layered architecture: ingest raw telemetry into ClickHouse, expose only aggregated/materialized views to the agent, and enforce runtime policy checks and output provenance to stop exfiltration and hallucination.

Blueprint overview — key components

High-level components and flow (most important first):

1. Telemetry ingestion: experiment logs, metrics, and artifacts are ingested into ClickHouse with schema tags for experiment_id, dataset_id, pii flags, hardware_id, and run_tags.
2. Aggregation layer: materialized views and aggregated tables (summary_metrics, failure_counts, noise_profile) expose only non-sensitive aggregates and precomputed histograms.
3. Agent orchestrator: a containerized AI agent (Cloud Run) receives a run trigger, queries the aggregation layer via a read-only API, and drafts the summary into a structured JSON report.
4. Guardrails & policy engine: enforces least privilege, query allowlists, rate/size limits, column redaction, and DP/noise for small counts before data hits the model.
5. Verification & provenance: the agent attaches citations of executed queries, raw hash references (or signed snapshots), and a validation pass to reduce hallucinations.

Design patterns — safe access to ClickHouse

1) Least-privilege roles and query surface

Never give the agent direct access to raw tables. Instead:

• Create a dedicated read-only ClickHouse user (agent_user) restricted to the aggregation schema.
• Publish only materialized views (example: run_summary_vw, noise_histogram_vw). These views should precompute all aggregations the agent needs.
• Use column-level metadata to mark sensitive fields and hide them from the agent role.

2) Materialized views + upward-only transforms

Materialize metrics that are stable and bounded in size (e.g., percentiles, aggregated counts, top-k error message hashes). Keep row counts predictable—this ensures responses are small and easy to audit.

3) Query allowlist and parameterized templates

Provide the agent with a limited set of parameterized SQL templates it can fill. A typical template:

SELECT metric_name, quantile(0.5)(value) AS p50, quantile(0.95)(value) AS p95
FROM run_summary_vw
WHERE experiment_id = {experiment_id} AND time >= {start} AND time < {end}
GROUP BY metric_name
ORDER BY metric_name;

The orchestrator fills and validates parameters (types, ranges) before execution to prevent injection-like behavior.

Agent architecture & safe tooling

Minimal toolset: SQL executor + evidence store

Design the agent with minimal tools—only a safe SQL executor (for materialized views) and an evidence store client that can fetch pre-signed, read-only artifacts (logs, sensor dumps) with strict TTLs. Avoid providing arbitrary shell access or file-system mounts.

Agent flow (step-by-step)

1. Trigger: experiment pipeline sends a webhook with experiment_id to an orchestration service.
2. Orchestrator: spins a short-lived Cloud Run container (or reuses a warm pool) and provisions ephemeral credentials for ClickHouse read-only access.
3. Preflight: orchestrator enforces policy—checks that experiment_id is valid, determines which materialized views are allowed, and loads a summarization prompt template that expects evidence citations.
4. Query & redact: the agent runs only allowlisted, parameterized queries. The policy engine redacts small counts or flagged PII and applies differential privacy where necessary.
5. Draft: the agent drafts a structured JSON summary with a mandated evidence array that includes the executed SQL, query results (bounded), and hashes of source artifacts.
6. Verify: a secondary verification step re-runs critical checks (low-temperature model or deterministic rule engine) to validate claims and ensure no hallucinated facts are present.
7. Publish: final report is stored in object storage (GCS/S3) and a metadata row is inserted into ClickHouse's reports table. Notifications are sent to collaborators.

Code examples — safe SQL execution and templating (Python)

Below is a simplified example implementing a parameterized, allowlisted query runner and a redaction step. This code uses a ClickHouse client and enforces max-result size and column allowlists.

import time
from clickhouse_driver import Client

CLICKHOUSE_HOST = 'analytics.internal'
AGG_SCHEMA = 'agg'
MAX_ROWS = 1000
PII_COLUMNS = {'user_id', 'email'}

ALLOWED_QUERIES = {
    'metrics_quantiles': (
        "SELECT metric_name, quantile(0.5)(value) AS p50, quantile(0.95)(value) AS p95"
        " FROM {schema}.run_summary_vw WHERE experiment_id = %(experiment_id)s AND time >= %(start)s AND time < %(end)s GROUP BY metric_name"
    )
}

client = Client(host=CLICKHOUSE_HOST)

def sanitize_params(params):
    if not isinstance(params.get('experiment_id'), str):
        raise ValueError('experiment_id must be string')
    if params.get('end') - params.get('start') > 7 * 24 * 3600:
        raise ValueError('time window too large')
    return params

def run_allowlisted_query(name, params):
    if name not in ALLOWED_QUERIES:
        raise PermissionError('query not allowed')
    params = sanitize_params(params)
    sql = ALLOWED_QUERIES[name].format(schema=AGG_SCHEMA)
    rows = client.execute(sql, params)
    if len(rows) > MAX_ROWS:
        raise RuntimeError('result exceeds max rows')
    return rows

# Example
params = {'experiment_id': 'exp-2026-0001', 'start': int(time.time()) - 3600, 'end': int(time.time())}
rows = run_allowlisted_query('metrics_quantiles', params)
print(rows)

Guardrails to prevent data exfiltration

1) Network and infrastructure controls

• Run the agent in a VPC with strict egress filtering. Only allow outbound connections to the LLM endpoint and your ClickHouse host.
• Use workload identity or short-lived service account tokens—no long-lived credentials in the container.
• Limit supported protocols to HTTPS and internal DB ports; block raw sockets or SSH to avoid lateral movement.

2) Data minimization and differential privacy

• Expose only aggregated data with bounded cardinality from materialized views.
• Apply differential privacy techniques (Laplace/Gaussian noise) when counts are below a threshold; return buckets instead of exact values.

3) Output sanitization and secret scanning

• Scan the agent's output for secrets/keys (regex matching for API keys, tokens, emails) and redact any matches before publishing.
• Enforce an output schema (JSON with fixed fields) to prevent free-text exfiltration of structured secrets.

4) Auditability and immutable evidence

• Log every executed SQL, parameter set, and resulting row counts to an append-only ClickHouse table or WORM object storage.
• Store cryptographic hashes (SHA256) of any raw artifacts referenced in the report so readers can verify claims without exposing full datasets.

Hallucination mitigation strategies

Evidence-first answers and deterministic rendering

Require the agent to present evidence for any factual claim. The report must include an evidence list where each claim references the SQL id and a bounded result excerpt. Use deterministic (low-temperature) model settings for the final pass.

Two-step composition + verification

1. Draft: the model produces a draft with placeholders where precise numbers should be cited (e.g., {p95_latency}).
2. Resolve: orchestrator executes the referenced queries and injects exact values into the draft.
3. Validate: a second model or rule engine re-checks that narrative sentences match the injected numbers and rejects mismatches.

Human-in-the-loop flags

For high-risk reports (PII exposure risk, large changes, or anomalies), set the CI/CD workflow to require human review before publication. Flag these reports automatically when thresholds or redaction events occur — a practical governance pattern aligns with guidance on FedRAMP and procurement considerations.

Deployment example: Cloud Run + Workload Identity

Quick deployment checklist for GCP Cloud Run:

1. Build the container (orchestrator + allowlist + validator).
2. Use Workload Identity to grant the Cloud Run service account minimal access to Secret Manager (for ClickHouse certificates) and to GCS for report storage.
3. Configure Serverless VPC connector and egress firewall to restrict outbound traffic.
4. Set concurrency and max instances to control resource usage and limit blast radius.
5. Enable structured logging and export logs to a SIEM for audit.

# Dockerfile (excerpt)
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "orchestrator.py"]

CI/CD integration — auto-generate reports on run completion

Embed report generation as a step of your experiment CI/CD pipelines to make summaries discoverable and versioned. Integrating the orchestrator into your pipelines is part of a broader developer-experience approach that makes agents first-class citizens in experiment workflows.

GitHub Actions example (conceptual)

Trigger the report workflow when the experiment job finishes. The workflow calls the orchestrator endpoint with the run id. The orchestrator runs the agent, stores the report in GCS/S3, and inserts a reference into ClickHouse.

name: post-run-report
on:
  workflow_run:
    workflows: [experiment-run]
    types: [completed]

jobs:
  report:
    runs-on: ubuntu-latest
    steps:
      - name: Call report service
        run: |
          curl -X POST https://orchestrator.example.internal/generate \
            -H "Authorization: Bearer ${{ secrets.ORCH_API_TOKEN }}" \
            -d '{"experiment_id": "exp-2026-0001"}'

Sample JSON report schema (strictly enforced)

{
  "report_id": "rpt-2026-01-18-0001",
  "experiment_id": "exp-2026-0001",
  "summary": "High-level narrative limited to 4 sentences",
  "metrics": [
    {"metric_name": "p95_latency", "value": 123.4, "unit": "ms", "evidence_sql_id": "sql-42"}
  ],
  "anomalies": [
    {"type": "regression", "description": "p95 latency up 12% vs baseline", "evidence_sql_id": "sql-43"}
  ],
  "evidence": [
    {"sql_id": "sql-42", "sql": "SELECT ...", "rows": [...], "row_count": 12, "hash": "sha256:..."}
  ],
  "signed_hash": "ed25519:...",
  "published_at": "2026-01-18T12:34:56Z"
}

Operational tips & advanced strategies

1) Schema-aware prompts

Include a brief schema description in the prompt so the model can reason about column semantics without getting raw values. For example: "run_summary_vw has metric_name, value, time_iso, hardware_id (hashed)".

2) Use hashing and tokenization for sensitive strings

Instead of returning raw error messages, provide HMAC/hashes of messages plus counts. This helps identify recurring patterns without revealing content.

3) Progressive disclosure

Start with aggregate summaries. If a reviewer requests drill-down, require an explicit human approval step that temporarily widens access for investigation.

4) Monitoring for agent drift

Regularly audit agent output quality by sampling and comparing generated claims to raw query results. Track metrics like evidence coverage (fraction of claims citing a query) and hallucination rate. Tie those telemetry metrics back into your vendor selection and trust models (see trust scores for telemetry vendors).

Case study — reproducible summary workflow (real-world pattern)

Team X runs nightly quantum simulator batches. They implemented the full stack above in Q4 2025:

• Ingested telemetry into ClickHouse with experiment tagging.
• Built materialized views that compute percentiles, error buckets, and hardware failure rates.
• Deployed an orchestrator in Cloud Run that only used allowlisted SQL templates and enforced DP on small counts.
• Added a verification pass that required human review for anomalies above 10% change.

Results: time-to-first-insight dropped from 6 hours to 25 minutes; the team found regressions faster while blocking any accidental exposure of PII or API keys because of the sanitizer and output schema enforcement.

2026 trends & future predictions

Expect these trends to shape agent-backed reporting:

• More OLAP-first observability: Databases like ClickHouse will become central to experiment reproducibility workflows as they add features tailored to analytics and secure materialized views.
• Agent governance frameworks: By 2026, governance tooling (allowlists, signed evidence standards, audit pipelines) will be a first-class component of AI orchestration platforms.
• Standardized report schemas: The community will converge on structured report schemas for experiment summaries that make automated ingestion and validation easier across labs.

Quick checklist before you enable agent-driven summaries

• Have materialized views for all agent queries.
• Lock down a read-only agent role with no raw-table access.
• Implement output schema, secret scanning, and DP redaction.
• Maintain an immutable evidence log and sign final reports.
• Require human approval for high-risk or high-impact reports.

Final actionable takeaways

• Start small: expose 2–4 aggregations the agent can use and iterate.
• Use an allowlist + parameterized queries—never free-form SQL from an agent.
• Make every claim citable: attach SQL IDs and bounded result excerpts to stop hallucinations.
• Protect the path: network egress, short-lived credentials, output scanning, and DP for small counts are mandatory.

Call to action

If you maintain experiment analytics, try this pattern on a single pipeline: build a materialized view, deploy a Cloud Run orchestrator with one allowlisted query, and automate a gated report. Start with non-sensitive runs to validate guardrails, then expand. Need a starter repo or a review of your ClickHouse schema and agent allowlist? Reach out to the qbitshare community or request our reproducible-reporting checklist to accelerate safe automation.

Want a packaged starter? We offer a reference Cloud Run image, ClickHouse materialized view templates, and a GitHub Actions demo to get you from zero to audited run summaries in under a day.

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