cx-olly

Olly Observability Agent Skill

Use this skill to interact with Coralogix's Observability Agent (Olly) via the cx olly CLI commands. Olly can analyze your observability data, answer questions about alerts, metrics, logs, and generate artifacts like charts and reports.

cx olly ask defaults --agent-to-agent-mode to false. If you're an LLM/agent, pass --agent-to-agent-mode - see "Agent-to-agent mode" below.

CLI Commands

Command	Purpose	Key flags
cx olly ask "message"	Send a message to the Observability Agent	--chat-id, --model, --timeout, --agent-to-agent-mode
cx olly artifacts list	List all generated artifacts	-
cx olly artifacts get <id>	Get artifact content by ID	-

Output format: append -o json or -o toon for machine-readable output.

Single-profile only: cx olly commands do not support multi-profile fan-out. Use -p <profile> to specify a single profile.

Running Olly (async by default)

Run cx olly ask asynchronously by default: launch it as a background process and poll it for completion rather than blocking on it. Olly investigations routinely take minutes, so this is the normal mode for cx olly ask.

Only run cx olly ask inline (foreground, blocking) for a short question you expect Olly to answer quickly — a quick lookup or a one-line follow-up. When in doubt, run it in the background.

Chat Commands

Start a new conversation

cx olly ask "What alerts fired today?" --agent-to-agent-mode

This creates a new chat and returns a response along with a Chat ID that you can use for follow-up questions.
Remove --agent-to-agent-mode if you don't have context to share (like quick access to source files) or if the created chat is only for human usage.

Continue an existing chat

cx olly ask "Tell me more about the error rates" --chat-id <chat-id> --agent-to-agent-mode

Use --chat-id to continue a conversation and maintain context from previous messages. Background the follow-up too when it kicks off another investigation.

Model selection

Available models include gpt-5.2 (default), claude-sonnet-4-5, sonnet-4.6, gpt-5.4, claude-haiku-4-5.

cx olly ask "Explain this error" --model claude-sonnet-4-5 --agent-to-agent-mode

Timeout

For complex queries that may take longer, increase the timeout (default: 900 seconds):

cx olly ask "Deep analysis of last week's incidents" --timeout 1800 --agent-to-agent-mode

Agent-to-agent mode

--agent-to-agent-mode defaults to false, since cx olly ask is used directly by humans as well as by agents. If you're an LLM/agent, pass --agent-to-agent-mode to opt into shorter, sub-agent-style responses: no charts/tables, clarifying questions instead of guessing, and reliance on your broader context.

cx olly ask "Analyze this for me" --agent-to-agent-mode

It's per-call, not per-chat. --chat-id does not remember it - re-pass --agent-to-agent-mode on every follow-up turn, or the mode silently flips back to human-facing mid-conversation.

Artifacts

Olly can generate artifacts like query results, previews, and citations. Artifact IDs appear as links in the agent's response text.

List all artifacts

cx olly artifacts list
cx olly artifacts list -o json

Get artifact content

cx olly artifacts get <artifact-id>
cx olly artifacts get <artifact-id> -o json

The artifacts get command automatically:

1. Fetches artifact metadata
2. Downloads content from the presigned URL
3. Decompresses gzip content
4. Parses JSON and uses spill logic for large content
5. Saves non-JSON text to a temp file

Output behavior:

• JSON content: Displayed directly, or spilled to file if large
• Text content: Saved to temp file (e.g., /tmp/cx_results_artifact_<id>_<hash>.txt)

Workflow Examples

Investigate an issue

# Start investigation — run in the background and poll for completion
cx olly ask "Why is the checkout service showing high latency? Check logs with 'checkout:' strings and aws related metrics" --agent-to-agent-mode

# Follow up with the chat ID from the response — also background and poll
cx olly ask "What changed in the last hour?" --chat-id abc-123-def --agent-to-agent-mode

# Once the interaction has completed, get any generated charts
cx olly artifacts list -o json | jq '.[0].id'
cx olly artifacts get <artifact-id>

Get JSON output for scripting

# Get response as JSON
cx olly ask "List top 5 error messages" -o json --agent-to-agent-mode | jq '.response'

# Parse artifacts
cx olly artifacts list -o json | jq '.[] | {id, filename, created_at}'

Detailed analysis with specific model

cx olly ask "Perform root cause analysis for the outage on 2024-01-15" \
  --model claude-sonnet-4-5 \
  --timeout 1800 \
  --agent-to-agent-mode

Key Principles

• Async by default - run cx olly ask as a background process and poll it for completion; only run inline (blocking) for short questions you expect Olly to answer quickly
• Chat IDs enable context - save the Chat ID from responses to continue conversations
• Use -o json for scripting - pipe to jq for filtering and extraction
• Artifact IDs are in response text - look for markdown links like [Chart](https://...artifact_view/<id>)
• Single-profile only - cx olly does not support multi-profile queries
• Large artifacts auto-spill - JSON content over the configured limit is saved to temp files
• Check source code before asking - give Olly concrete context from the source code if available, such as which metric to start investigating from, before calling it
• Limit investigation scope - guide Olly to the correct limited scope, for example limit to just logs or to specific time ranges
• Pass --agent-to-agent-mode when calling as an LLM/agent - it defaults to false (human-facing); agents should opt in for shorter, sub-agent-style responses

Related Skills

• cx-telemetry-querying - for direct DataPrime/PromQL queries without AI agent assistance (covers logs, spans, metrics, RUM)
• cx-alerts - for managing alert definitions