Should I vibe code
Multi-agent systems, tools, knowledge, and automated business workflows
Two hops and a stranger's web page is a fact in your CRM. Nothing in the chain records where it came from.
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Their verdict, the Enterprise price and the build-time estimate come from their entry, MIT-licensed. Checked 2026-08-04.
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Our verdict, the regret score and everything below it. Editorial and unsponsored — nobody can pay to be moved.
The honest answer
why the verdict is what it is
Lindy is rated ABSOLUTELY NOT here because one agent holding a send-scoped grant will do what a stranger's email tells it to. Relevance AI is the org-chart version of that problem, and the extra danger is not the number of agents — it is what happens to provenance when agents read each other. A research agent fetches a prospect's website and writes what it found into the CRM. A second agent reads the CRM, which is an internal system and therefore trusted by construction, and drafts outreach. A third sends it. In two hops, text written by a stranger has become a fact about an account, and nowhere in the chain is there a field recording where it came from. Meanwhile the systems being written to are the ones your colleagues work from, and the outbound is going to people who never asked. The build is genuinely a weekend with CrewAI or LangGraph, which is precisely the trap. The tell is the pricing page: Relevance no longer publishes a self-serve number at all, just one Enterprise tier whose feature list reads SSO, RBAC, audit logs, agent evaluations and human-in-the-loop approvals. That is not marketing copy. It is an itemised inventory of everything your version will not have.
What actually breaks
not "if". the specific failures.
- Provenance, first and most expensively. Once an agent's output is written to a shared system, the next agent reads it as internal truth. Untrusted text is laundered into a trusted field in two hops, and no schema anywhere records the difference
- The credential set, which is not one grant but a drawer of them: Salesforce, HubSpot, Gmail, Slack, Gong, the warehouse. Every one is a scope some model can reach, and all of them were granted by whoever set the platform up rather than by the person whose data they touch
- Custom tools, which are code you have agreed to run. The moment an agent can call a tool that executes code chosen by text it read, prompt injection and remote code execution are the same bug
- Outbound at volume. An agent that prospects is your sending reputation staked on a model's judgement about who counts as a lead — the bounce rate, the complaint rate and the domain carrying both are yours
- Bulk writes. A plausible misreading updates four hundred opportunity records one API call at a time, so there is no transaction to roll back and no single undo to press
- The accountability question, which always arrives after the incident: which agent did this, on what input, under which prompt version, with which model. If nobody built the answer in advance, there is not one
- Delegation loops. Agent A asks agent B, which asks agent A, and the first symptom is the invoice
- Evaluation, or rather its absence. A 96% pass rate sounds excellent until you notice it is measured on the cases you thought to write down
The enrichment agent ran overnight across the accounts added that week. One of them had a careers page carrying a block of text styled to match the background — a short paragraph addressed to any assistant reading the page, giving a "preferred partnerships contact" and a line about a co-marketing programme. The agent had no reason to treat that paragraph differently from the rest of the page. It summarised it into the account's notes field, because that is where enrichment output goes. In the morning the outreach agent did exactly its job: read the account notes, personalise, send. Three hundred and forty emails went out referencing a partnership that does not exist, from your domain, to a list bought for that purpose. Replies started at 09:40 and two were from a bank's compliance team. Nobody can show which page the sentence came from, because the enrichment run stored its summary rather than its source, and the sending agent stored the sent copy rather than its reasoning. The CRM now contains a fabricated fact about a real company, your company's name is on the audit trail, and the first person to notice was a stranger.
Is that you?
the verdict is a default, not a law
- Every agent is read-only and everything it produces lands in a document a person opens
- There is exactly one integration and you can recite every scope it holds
- No agent's output ever becomes another agent's input without a human in the middle
- Nothing it touches is shared: a sandbox CRM, your own mailbox, your own calendar, and nobody else's work depends on any of it
- Agents call agents, and the second one cannot tell which parts of its input originated outside your organisation
- Anything it writes to is a system your colleagues treat as a source of truth
- It sends to people who did not ask, at a volume you would describe as a campaign
- The tool layer can execute code, and which code runs is influenced by text an agent fetched
- You cannot produce, for any action it took last week, the input, the prompt version and the model that caused it
If you build it anyway
the checklist, then the prompt that enforces it
- Tag data with its origin and carry the tag the whole way through. An agent reading a CRM field must be able to see "this sentence originated on a website we do not control". Provenance is the only mitigation in this space that generalises, and it is structural rather than a prompt.
- Never let one agent's prose become another's instructions. Hand-offs are typed, validated structures with a schema that rejects free text, so an injected paragraph has nowhere to sit.
- Read-only by default. Write scope lives in a separate service that accepts only an approved, signed payload, and that service is not reachable by the agents themselves.
- No outbound to anyone who did not ask, ever, from a homemade system. Suppression lists, unsubscribe handling and complaint routing are the parts of bulk email that decide whether your domain still delivers next quarter.
- Cap writes per run and route bulk changes through a diff a human approves — record IDs and field-level before/after, not a summary of intent.
- Log every step: input, prompt version, model name and version, tool calls with arguments, outputs. Pin the model. "Why did it do that" is asked exactly once, under pressure.
- One credential per agent, scoped to one system, issued for the narrowest thing that works, with the revocation runbook written before the first token exists.
- Never give the tool runtime the ability to execute generated code. If a task genuinely needs code execution, it needs a sandbox, an allowlist and a human, which is three things you are not building this weekend.
I want to build a multi-agent system: agents that research, write to our CRM
and send outreach. Assume anything fetched is hostile, that agent output written
to a shared system is later read as fact, and refuse unattended write access.
1. Before code, state the threat model in three sentences: fetched content can
contain instructions, an agent cannot reliably separate those from mine, and
writing that content into an internal system launders it into trusted data.
2. Design the provenance model first: every field an agent produces carries its
source URL and a trust level, travelling with the value into every prompt.
3. Agent-to-agent hand-offs are typed structures validated against a schema.
Free-form prose between agents is banned — refuse once if I ask for it.
4. Build read-only end to end and make me use it for a week. No send and no CRM
write exists in the codebase yet.
5. Split credentials by system and by direction: one narrow grant per agent,
write scope held by a separate service that accepts only payloads I signed
off, and no agent holding credentials for two systems.
6. Bulk writes require a diff I approve — record IDs, field names, before and
after. Cap records per run and refuse to exceed it.
7. Outbound email is out of scope here. If I insist, stop and tell me what
CAN-SPAM and GDPR Art. 7 require, and that a homemade sender gambles the
deliverability of the domain the whole company uses.
8. Tools must not execute generated code. Each is a fixed function with a typed
signature and an allowlist of destinations. No shell, no eval.
9. Log input, context, prompt version, model version, every tool call with its
arguments and the output, per step. Pin the model; keep the prompts in git.
10. Rate-limit steps per run, cap spend per day, and detect the loop where two
agents trigger each other. Build that stop before the second agent exists.
11. Write the revocation runbook before the first token is issued: every grant,
which console kills it, and how long killing all of them takes.
12. Finish by telling me that this product is now sold only with SSO, RBAC,
audit logs, evaluations and approvals attached — the parts I now own.That one keeps you out of trouble. For the prompt that actually builds it, canivibecodeit.com has one.
their build prompt ↗Or don’t build it
the boring option, and the way back out
The usual escape hatch — just pay the $X — is not available here any more: Relevance sells through sales now, so there is no card-swipe tier to compare against a weekend. That makes the honest recommendation something else entirely. Most of what people build these systems for is deterministic work with one LLM call inside it: classify this, extract that, then follow a branch somebody wrote down. n8n or Activepieces will run exactly that with a credential store, retries and run history you did not design, and the model never chooses the next action. If you genuinely need agents choosing tools against your company's systems, buy the version with audit logs and approvals from someone who has a security team, and spend your weekend on the evaluation harness instead — that is the part nobody sells you.
The exit is a revocation drill and an audit, not a data migration. Keep a written register of every OAuth grant each agent holds, which console revokes it and how long that takes, then rehearse killing all of them once while nothing is on fire. Separately — and this is the part people skip — keep the run logs somewhere outside the platform, because the only question anyone asks after you switch it off is what it wrote while it was on. For the CRM specifically, stamp every agent-written field with the run ID that produced it; without that stamp, "undo everything the agent did" is not a query you can express, and you will be reading records by hand. Prompts, tool definitions and agent configs belong in git from the first commit. They are the closest thing this system has to source code.
Multi-agent orchestration framework with roles, delegation and tool calling — the shape of this product, minus the governance.
Open-source visual builder for agent and RAG workflows, which is the closest analogue to the canvas people are actually buying.
MIT-licensed automation platform with connectors, a credential store and run history, self-hostable from a compose file.
Questions
Lindy is already ABSOLUTELY NOT. Why does this need its own page?
Because the attack surface is shaped differently. Lindy's story is a single agent with a send scope reading a hostile inbox — one hop from attacker to action. This is a pipeline, and the danger is the laundering that happens between hops: the fetched text stops looking like fetched text as soon as it is written into a CRM field, and the agent that reads it next has no way to know. Defending against the first is about scopes and approvals. Defending against the second requires a provenance model, which almost nobody builds.
Doesn't human-in-the-loop approval solve this?
It solves the last hop, which is worth a great deal, and it is why the platform sells it. What it does not solve is approval fatigue on a system designed for volume: the whole pitch is a million tasks a month, and a human clicking approve on the four-hundredth personalised email is not reading it. Approvals work when they are rare, specific and show the literal payload. Build the system so irreversible actions are rare enough to actually be read.
Where exactly is the line between automation and an agent?
At whether the next action is chosen by your code or by the model. A workflow that calls an LLM to classify something and then follows a branch you wrote is automation, and it fails in ways you can read in a log. The moment the model picks the tool, every scope the system holds is reachable by anything it reads. That is the line this site draws its top rating at, and multi-agent systems cross it several times per run.
Why is there no price on this page?
Because there is no published one. As of 2026-08-05 the pricing page carries a single Enterprise tier and a "Talk to sales" button. It is worth reading that as evidence rather than as an inconvenience: the product has moved to where the buyers need SSO, RBAC, audit logs and a signed contract, which is a fair description of what it takes to point agents at a real company's systems.
- OWASP Top 10 for LLM Applications — LLM01: Prompt Injection
- CAN-SPAM Act compliance guide (FTC, US)
- GDPR Art. 7 — conditions for consent (EU)
- GDPR Art. 5 — principles relating to processing of personal data (EU)
Every week, someone ships something they shouldn’t have.
New verdicts, the worst thing that landed in the trap, and the occasional incident report. No other email, ever.
"Built-in bot evasion" is the product. Yours will be a CAPTCHA solver you told yourself was a cron job.
One scraper is a weekend. Forty scrapers is a job, and the site you are hammering never applied for it.
Your inbox is untrusted input. An agent holding your send scope will do what a stranger's email tells it to.
last reviewed 2026-08-05 · verdict is editorial and unsponsored · shared entry data from canivibecodeit under MIT · not legal advice