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Platform Assistant

Waxell includes a built-in AI operations advisor that can query your observability data, create and manage policies, coach your team through setup, and proactively surface what needs your attention — all through natural language.

Open it from the Ask AI button in the top navigation on any page, or visit your assistant home at /assistant for a personalized briefing.

AI transparency

The assistant is an AI system, and the interface says so wherever you meet it — the AI badge in the dock header, the caption under the chat input, and the disclosure on the assistant home page. For the full system-information notice (purpose, operation, limitations, human oversight, data handling), see AI System Information.

The Assistant Dock​

The assistant lives in a dock on the right side of every page, with three tabs:

TabWhat it shows
ChatThe conversation — ask anything, get answers grounded in your actual data
FeedProactive notifications the assistant has surfaced for you — setup nudges, anomalies, and follow-ups, with an unread badge
PulseA live snapshot of fleet activity

The dock is page-aware: the suggested prompts change based on where you are. On the Experiments page it offers error analysis and cost hotspots; on Getting Started it offers setup guidance; on Governance it offers policy work. You can also reach the assistant from empty states, entity pages, and the command palette (⌘K).

Assistant Home​

Visit /assistant for your briefing page: what needs you, where you left off, and what to set up next. The For You section collects the assistant's proactive findings; Continue the conversation picks up your recent chats exactly where they stopped.

What It Can Do​

Query Your Data​

Ask questions about your agents, runs, and costs in plain language:

"How many runs did my agents do in the last 24 hours?" "What did I spend on LLMs today, broken down by model?" "Which agent has the highest error rate this week?" "What LLM routing is configured for my agent?"

The assistant queries your actual observability data and returns real numbers — run counts, per-model spend, error rates, session activity, experiment results — with inline charts for anything worth visualizing. If your fleet is clean, it says so and offers the follow-up that matters (like setting an alert), rather than burying you in empty tables.

Create and Manage Policies​

Policy work is conversational:

"Help me set up policies for my agents" "Create a rate limit policy for support-bot — max 50 runs per hour" "Explain this proposed policy"

The assistant reads your fleet first — which agents are active, what coverage already exists — then proposes interactive policy cards you can create with one click or open in the editor. It never creates a policy without your explicit approval. If you have no policies yet, it treats that as the gap and proposes a grounded starter set.

Card buttonAction
Create PolicyCreates the policy on the control plane
Open in EditorOpens the full policy editor with the config pre-filled
Suggest AdjustmentsAsks the assistant to modify the policy
Ask About This PolicyGet more context on what the policy does

Take Action​

Beyond policies, the assistant proposes small concrete actions as action cards:

  • Setup steps — adds items to your personal setup plan (the one write it performs directly, always with your approval on the card)
  • Invite teammates — preps who to invite and why, and links you to user management
  • API keys — links you to key creation (the assistant never mints or displays keys itself)
  • Alert rules — drafts the condition, threshold, and destination, and links you to alerts

Card approval is the human-in-the-loop: nothing executes until you click.

Learn the Platform​

Ask how anything works and the assistant answers from the official documentation, with citations:

"How do I instrument a Python agent with waxell-observe?" "What is the MCP gateway and when should I use it?" "My agent runs aren't showing up in Observe — what should I check?"

Troubleshooting answers lead with the most likely cause and link every claim to its source page.

"Where do I manage API keys?" "Take me to where I can see my LLM costs" "What does this page do?"

The assistant knows every page in the product and answers with direct links — plus practical notes about what you'll find there.

Proactive Layer: Feed and Digest​

The assistant doesn't just answer — it watches. An hourly evaluation looks at your workspace for things worth telling you about: unfinished setup, new failure patterns, spend changes, and coverage gaps. Findings land in your Feed with an unread badge, and a weekly digest email summarizes the highlights.

Each notification links to a pre-filled conversation so you can act on it in one click.

Personalized to Your Role​

Answers adapt to who you are:

  • Capability-aware — the assistant only offers what your seat and role allow. It will never propose policy creation to someone without governance permissions, or quote billing to a seat without billing access.
  • Register-aware — builders get code and exact config keys; operators get rollout steps and checklists; executives get the trend and the business impact in two sentences.

Memory​

The assistant remembers context across conversations — who you are, what you were working on, and the preferences you've expressed — so follow-up sessions start warm. Ask "what do you already know about me?" any time to see (and prune) what it holds.

Privacy and Security​

Every conversation runs under a short-lived, cryptographically signed identity token minted for you at message time:

  • Tenant-scoped by construction — the assistant's data access is derived from verified token claims, never from anything a caller supplies. It cannot read another workspace's data, and it will tell you so if you ask it to.
  • Least-privilege — its data access is read-only; the only writes it performs (policy creation, setup-plan steps) require your explicit approval and are attributed to you in the audit trail.
  • No internals — the assistant presents everything in business terms and declines requests for platform implementation details.
  • Conversation history is stored per-user in your own workspace.

Escalation​

If the assistant can't resolve something, use Talk to a human at the bottom of the dock — your conversation transcript rides along so you never repeat yourself.

API Access​

The assistant is also available via API for programmatic integration:

# Send a message
curl -X POST -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"message": "How much did my agents cost today?"}' \
  "https://acme.waxell.dev/api/v1/assistant/chat/"

# Poll for response
curl -H "Authorization: Bearer $TOKEN" \
  "https://acme.waxell.dev/api/v1/assistant/chat/{signal_id}/result/"

# Get conversation history
curl -H "Authorization: Bearer $TOKEN" \
  "https://acme.waxell.dev/api/v1/assistant/chat/history/"

Next Steps​