Ambient agents
for your codebase
Define automated agents that continuously maintain and improve your code. Dependency upgrades, feature flag cleanup, dead code removal — running while you sleep.
Trigger-based
Cron, webhooks, Slack, PagerDuty
Sandboxed
Safe execution in isolated environments
MCP-native
Connect Datadog, Sentry, Slack, and beyond
Features
Everything an ambient
agent needs
Trigger-driven
Cron schedules, GitHub webhooks, Slack messages, PagerDuty incidents, or manual runs. Each trigger scoped to a repo and branch.
Natural language instructions
Write plain English prompts with @-mentions for tools and MCPs. The agent resolves references and executes autonomously.
Plug-and-play MCPs
Connect Datadog, Sentry, Slack, Databricks, and any custom MCP server. Agents get typed access to your entire toolchain.
Sandboxed execution
Every run happens in an isolated environment. OS-level sandboxing for local dev, containers for production. Safe by default.
Memories and skills
Agents learn across runs with persistent memories. Skills are reusable instruction sets that encode domain knowledge.
PR-native output
Automations open pull requests, post to Slack, create issues. Review agent work like any other contributor.
Use Cases
Automations that actually
run your playbook
Dependency upgrades
Scans for outdated packages, attempts upgrades, runs your test suite. Opens a PR for each successful upgrade with changelog notes.
Feature flag cleanup
Detects flags that are fully rolled out and stale. Removes the flag, dead code paths, and opens a clean PR.
Incident triage
Pulls relevant logs and metrics from Datadog, traces the error, and posts a structured analysis to your Slack on-call channel.
User feedback to code
Reads feature requests from Slack, checks for duplicate Linear issues, and either implements or summarizes the work needed.
How It Works
Three steps to
autonomous maintenance
Define your automation
Use the visual editor to configure triggers, write natural language instructions, attach tools and MCP servers, and choose a model.
# Instructions Use @mcp:datadog to investigate this incident. Search for relevant logs and metrics. Post findings to @action:slack #oncall.
Agent runs in a sandbox
When a trigger fires, Caret spins up an isolated execution environment, clones your repo, and runs the agent with full tool access.
Trigger fired: cron (Mon 09:00) Sandbox: creating... Cloning repo: acme/backend (main) Agent: loading skills, memories Agent: executing instructions...
Review the results
The agent opens PRs, posts to Slack, creates issues. Every run is logged with diffs, summaries, and full execution traces you can audit.
Run #42 completed (3m 21s) Files changed: 4 PR opened: #1847 "Upgrade lodash 4.17→4.18" Tests: 142 passed, 0 failed Slack: posted to #platform-updates
Stop maintaining.
Start shipping.
Let ambient agents handle the toil so your team can focus on what matters.