AgenticOps automation · Code

Automate Feast code review and CI/CD remediation with AgenticOps

Feast sits on the path between a proposed change and production — where a coding tool writes the diff but nothing safely ships it. AgenticOps is the discipline of running production cloud operations through autonomous AI agents — under team policy, with brokered credentials, sandboxed execution, deterministic data tokenization, and tamper-evident audit — so Feast reviews, PRs, and pipelines get remediated on the loop instead of stacking up in a queue.

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Grounded in your stack

Controlled by your policy

Verified after every action

The operational work this removes

Code review and PR queues pile up faster than senior engineers can drain them, so risky diffs merge unreviewed and low-risk ones wait days
CI/CD pipelines fail on the same recurring causes — flaky tests, dependency drift, expired credentials — and each failure burns an on-call engineer's afternoon
Dependency and secret hygiene is reactive: CVEs and leaked tokens are found long after the vulnerable commit already shipped
IaC drift between the Terraform state and the live account accumulates silently until an audit or an incident surfaces it
CI runners hold broad, long-lived credentials that any compromised job can abuse, and there is no per-job audit of what those credentials touched
SOC 2 / change-management evidence is assembled by hand from Slack threads and PR comments because no tamper-evident record ties a change to its approver

From signal to verified action

CloudThinker investigates the signal, proposes or executes the safe action your policy allows, then verifies the outcome.

01 · Detect

Detect the Feast signal

The Feast connection continuously watches for review-worthy and pipeline-blocking signal — new pull requests, failing CI/CD stages, drifted IaC plans, newly disclosed CVEs in the lockfile, and secret-scanner hits — and normalizes each into a scored finding with a repo, branch, blast radius, and owning team.

02 · Analyze

Analyze the root cause

Each finding is enriched with the surrounding context an experienced reviewer would pull: the diff, the failing test history, the dependency's changelog and CVSS, the Terraform plan delta, and prior related PRs. The agent classifies root cause and produces a risk score, keeping brand and product data (repo contents, secrets, account IDs) tokenized before any prompt crosses a third-party LLM boundary.

03 · Remediate

Remediate under policy

The agent opens a scoped merge request with the fix — a pinned dependency bump, a quarantined flaky test, a corrected IaC resource, a rotated secret reference, or an inline review comment — plus the rationale and the exact commands it would run. Execution happens inside an ephemeral sandbox with a short-lived, least-privilege token issued at task time, never a shared CI credential.

04 · Verify

Verify and record

After the change, the agent re-runs the pipeline stage or plan, confirms the finding is cleared and no new failures were introduced, and writes a tamper-evident record — request, diff, tool calls, test results, and approver — into the audit log. Engineers stay on the loop: every action runs at the autonomy level (L1 Notify → L4 Autonomous) the team set for that repo and environment.

Evidence and proposed action

$ cloudthinker fix-plan --skill "Feast" --pr 4821

Detected   PR #4821 "bump service deps" — 2 blocking findings
Analyzed   • lodash 4.17.19 → CVE-2021-23337 (CVSS 7.2, prototype pollution)
           • terraform plan drift: aws_s3_bucket.logs public-read re-enabled
Risk       HIGH · touches prod IAM + public storage · owning team: platform

Proposed remediation (graduated autonomy: L2 · act-with-approval)
  1. bump lodash → 4.17.21 (patched), regenerate lockfile
  2. revert bucket ACL to private, restore aws:SecureTransport policy
  3. re-run pipeline stages: install → test → tf-plan

Execution
  sandbox    ephemeral microVM · syscall-filtered
  identity   ci-agent@Feast · scoped token (repo:write, expires 15m)
  data       repo + tf state tokenized before LLM egress

Verify
  ✓ npm audit — 0 high/critical
  ✓ tf plan — no drift · bucket private
  ✓ 214/214 tests pass · no new failures
  ↳ merge request opened · awaiting approver · audit id a1f9c-4821

Engineer on the loop — approve, edit, or reject.

What the agent understands

Feast feature store management. Covers feature store configuration, entity management, feature views, materialization, online serving, and data source inspection. Use when managing ML feature pipeline

Related automations

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Issue Context ReviewEnrich code reviews with business context by finding the linked issue/ticket from branch name or PR/MR description. Validates that code changes satisfy acceptance criteria and business requirements before reviewing.GitLab Issue from ReviewCreate GitLab issues from code review findings. Converts review comments into trackable issues with severity labels, code references, and remediation guidance.GitHub PR Review ChecklistPost structured review checklists as comments on GitHub pull requests. Summarizes code review findings with categorized checks, severity indicators, and actionable feedback.SonarQubeSonarQube code quality and security analysis. Use when working with code quality metrics, security hotspots, quality gates, or issue tracking in SonarQube Cloud or Server.BitbucketREQUIRED helper functions (never raw curl), workspace/repo_slug patterns, suggestion block syntax for code review GithubREQUIRED --repo flag for all commands, helper functions for code review comments, suggestion block syntax rules

See what AgenticOps can run safely in your stack.

Connect CloudThinker to map the signals, tools, and runbooks already in your environment. You choose the approval level; every action stays attributable and auditable.

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