Give your agent a second opinion it didn't write itself
The AI Advisor Lab MCP server connects your agent to a curated team of domain advisors. One call convenes the panel: they analyze in parallel, respond to each other's positions, and return a single synthesized answer with its claims tagged.
Your coding agent reviews its own work
Which means its one blind spot is itself. An agent that drafts a schema, a migration plan or a security model will assess that work using the same assumptions that produced it. This is not a talking point — independent 2026 research on real agent runs and production codebases measured it.
Agentic coding can burn up to 1000× the tokens of a normal code chat, mostly re-reading its own accumulated context at every step. Past a point more tokens don’t improve accuracy, and the same task can swing 30× in cost.
Stanford Digital Economy Lab · Bai et al., 2026Code-block duplication is up 81% since 2023 and refactoring is down 70%, while two-week churn is up 15% — code the agent writes, then rewrites within weeks.
GitClear “The Maintainability Gap: AI Code Quality in 2026” (623M analyzed changes, 2023–2026)Updates to existing code have fallen 74% since 2023, and copy-paste now beats refactoring 5 to 1 — reversed from a 2× preference the other way in 2022, before AI. Function connectivity, how often new code calls into existing code, is down 35%.
GitClear “The Maintainability Gap: AI Code Quality in 2026” (623M analyzed changes, 2023–2026)Across 150+ models, only 55% of generation tasks produce secure code — 45% introduce a known security flaw, even as syntax correctness exceeds 95%. Newer, larger models are no safer. They miss the flaws that need reasoning across several files.
Veracode Spring 2026 GenAI Code Security Update (150+ models · 80 tasks · four CWE types)The takeaway isn’t “stop using coding agents.” It’s that an agent’s own confidence isn’t a review. A model checking its own work carries the same blind spots that wrote the flaw. Read the full analysis in Are your coding agents wasting tokens or wrecking their own code? and Who checks your AI agent’s work?
Where AI Advisors fit alongside your AI Agents
Your agents keep working exactly as they do today. At the two moments where judgment matters most — before committing to a direction, and before finalizing the result — the agent, or your team, consults the advisory panel, then verifies and proceeds.
Advice only. They recommend; they never touch your systems.
Full access to your files, systems and data, exactly as today.
Why this is genuinely a second opinion
Most agents can already “assemble a review team.” The difference is what sits behind it.
One model in several costumes
Having one model play several roles is fast, free and useful — but every reviewer is the same model wearing a different hat. They share the same blind spots, so what one misses, they all miss. The review reads confident either way, because there is no independent check behind it.
Separate advisors, engineered dissent
Each advisor runs as its own call, and the panel is built to disagree rather than to agree:
- A second round where advisors respond to each other’s positions
- A designated devil’s advocate, and a contrarian pass on demand
- Evidence gathered across more than one research model, so the panel isn’t limited to one model’s training data
- Every claim labeled verified, estimated or unconfirmed, with an overall grounding score
Two ways teams use it
The same connection either way. What changes is who is calling.
A review panel in your editor
Connect the coding agent your team already uses and ask an advisory team to examine the decision it just made — the architecture, the security model, the migration plan — before it becomes permanent. Ask for the case against a direction you have already chosen, or a review of a specific piece of work.
Advisory as a step in your workflow
Your application sends its own data and receives a structured result, so a panel review becomes a stage in your pipeline rather than something a person runs by hand. Long reviews run in the background and are collected when ready, so nothing has to hold a connection open. Each claim comes back labeled, so you can present a verified finding differently from an estimate.
How you connect
AI Advisor Lab connects through the Model Context Protocol — the standard way agents reach outside tools. Fifteen agent platforms have been validated against their vendor documentation, and any client that supports remote MCP servers connects the same way.
Sign in through the browser
One sign-in with your account. Nothing to copy or store.
- Claude Code
- Cursor
- Windsurf
- n8n
- Perplexity
- Microsoft Copilot Studio
- Salesforce Agentforce
Connect without a browser
For servers, CI and autonomous agents, using a revocable token issued from your account.
- CrewAI
- OpenAI Agents SDK
- Google Vertex AI ADK
- LangChain / LangGraph
- Microsoft AutoGen
- OpenAI Responses API
- Manus
Setup takes about a minute. We provide the full integration runbook — step-by-step configuration for your platform, and the patterns for embedding advisory into your own application — once your account is set up.
What your agent can ask for
Sixteen capabilities are available over the connection. In practice most work runs through two: find the right team, then consult it.
Find the right team 4 capabilities
Your agent describes the problem in plain language and the best-matched advisory team is selected and loaded for it. It can also browse or search the full catalog of 270+ teams across 17+ industries and 40+ industry subsectors, and check which team is currently in use before it asks anything.
Consult the panel 3 capabilities
Your agent puts a question to the whole advisory team and receives one synthesized answer. It can request an added round in which the advisors challenge each other directly, or ask specifically for the case against a decision that has already been made.
Review a specific piece of work 1 capability
Rather than asking a general question, your agent can submit a particular implementation for review and get the panel’s assessment of that work on its own terms.
Handle long-running reviews 3 capabilities
Your agent starts a deeper review, carries on with other work, and collects the finished result when it is ready. It can also list the reviews it has run, and cancel one that is still in progress if the question has moved on.
Carry your business context 2 capabilities
Your agent can read and update the working picture of your business that the teams hold — your industry, competitors, regulatory posture and current initiatives — so a later consult already has that context instead of starting cold.
Check the answer it was given 3 capabilities
Your agent can score a response for trait risk such as sycophancy, trace conclusions back to the advisors and evidence that produced them, and see how the panel is composed before deciding how much weight to give the advice.
What to expect when you call it
It takes longer than a single model call, on purpose
You are not getting one response. You are getting several advisors analyzing the question, then responding to each other's positions before anything is synthesized. That deliberation is the product, and it takes time. A quick review comes straight back. A deeper one is acknowledged in about a second and then runs in the background — typically around 10 to 25 minutes at standard depth. Your agent carries on with other work in parallel and collects the result when it lands, so nobody sits watching a spinner.
A consult uses one report from your plan
A consult over MCP draws on the same monthly report allowance as a meeting run in the web app — one consult, one report. Token costs for the run are billed to your own Anthropic account through the key you supply, so they appear on your Anthropic bill rather than ours.
What you need before you connect
Four things, stated up front so you don't discover them halfway through an evaluation.
A paid plan
MCP access is available on Professional, Enterprise and Partner plans. See pricing.
An account
Your teams, history and business context live in your workspace, so a consult is always scoped to you.
Your own Anthropic key
Added in your account settings. It pays for each run's tokens directly, with no markup from us.
MFA enabled
Required on your login, and required before a long-lived token can be issued for headless use.
How access is controlled
The questions that come up in a security review, answered plainly.
- Standards-based sign-in. OAuth 2.1 with PKCE and Dynamic Client Registration, published at the standard discovery endpoints so a compliant client can configure itself.
- Tokens are checked, not just accepted. Access tokens are validated against the expected audience, and only access tokens are honoured — an identity token presented in their place is rejected.
- MFA gates token issuance. A long-lived token for a headless agent can only be minted by a user who has enrolled in MFA. That check fails closed.
- Tokens are revocable and short-lived by policy. They expire within 90 days, can be revoked at any time, and are stored only as a hash — the original value cannot be read back from our systems.
- Administrative actions are recorded. Audit events are written to a hash-chained log so the record cannot be quietly altered, and that log is encrypted at rest with a managed key.
- Tenant separation at the application layer. Data is encrypted at rest and every consult is scoped to your workspace. Dedicated tenancy is available on request.
- Runs across multiple instances. The server runs behind a load balancer with more than one instance, so a consult is not tied to the health of a single process.
Put a panel behind your agent
Set up your account, add your Anthropic key, and connect your first client in a minute.