We tested 6 agent platforms in one week. Here's how we picked.

When our 30-person team decided to invest in AI agent infrastructure, we didn't want to pick based on marketing pages. We ran a one-week evaluation across 6 platforms and made the decision from data.

Here's the process and what we found.

The 6 platforms

→ Lindy → Relevance AI → Avery NXR → n8n (with AI nodes) → Zapier (with AI agent features) → Custom build (LangChain + OpenAI)

Other platforms in the space (CrewAI, Sierra, Decagon, etc.) we didn't evaluate because they targeted different use cases (multi-agent dev framework, customer support chatbots specifically, etc.).

The four workflows we tested

We picked four workflows that represented our actual operational needs:

A. Daily competitor monitoring. Watch 10 competitor URLs, identify meaningful changes, send weekly digest.

B. Inbound lead qualification. Form submission → research the company → score fit → draft response → route to right salesperson.

C. Support ticket triage. Incoming ticket → classify by topic + urgency → auto-respond to FAQ → route the rest with drafted response.

D. Meeting follow-up. Transcript → extract action items + decisions → send personalized email to each attendee.

We implemented all four on all six platforms. Same workflows. Same data inputs. Same success criteria.

Evaluation criteria

We scored each platform on:

→ Setup time (minutes to working version) → Output quality (graded on real samples by domain expert) → Cost projection (12-month total cost for our 30-person team) → Data flow (where did our data go to get processed) → Vendor lock-in (what happens if we stop paying) → Maintenance burden (how often we'd need to babysit it)

Results

Setup time (lower is better): → Lindy: 90 min for 4 workflows → Relevance AI: 110 min → Avery NXR: 75 min (templates pre-loaded helped) → n8n: 180 min (had to install + configure server) → Zapier: 95 min → Custom build: 8+ hours, still not feature-complete

Output quality (higher is better): → Lindy: 8.5/10 → Relevance AI: 8.7/10 → Avery NXR: 8.3/10 (local model on operational workloads) → n8n: 7.8/10 (depends on which AI node) → Zapier: 7.0/10 (AI features were thin) → Custom build: 9.0/10 (could tune more, but engineering cost)

For operational workflows, the spread was smaller than we expected. Frontier cloud LLM advantage was real but not as large as marketing suggested.

Cost projection (12 months, 30-person team): → Lindy: $35,000-50,000 → Relevance AI: $40,000-60,000 → Avery NXR Pro: $10,440 ($29 × 30 users × 12 months) → n8n self-hosted: $5,000 (server) + $15,000 (AI API costs) = $20,000 → Zapier: $25,000-35,000 (depending on task volume) → Custom build: $80,000-120,000 (engineering time + API costs)

Avery NXR was the lowest by a clear margin. The flat pricing model + zero AI cost made the math significantly different.

Data flow: → Lindy: their cloud → Relevance AI: their cloud → Avery NXR: our infrastructure (laptops or our cloud) → n8n self-hosted: our infrastructure + AI API providers → Zapier: their cloud → Custom build: our infrastructure + AI API providers

For our use case (mid-market, some regulated customers, security team that asks where data goes), the data flow factor mattered.

Vendor lock-in: → Lindy: high (if we stop paying, agents stop running) → Relevance AI: high → Avery NXR: low (drop to Free Desktop tier and agents keep running) → n8n: low (we own the install) → Zapier: high → Custom build: zero (we own everything)

Maintenance burden: → Lindy: low (managed service) → Relevance AI: low → Avery NXR: low-medium (need to update agents, manage local models) → n8n: medium (server + updates + scaling) → Zapier: low → Custom build: high (we're now an AI infrastructure team)

Our decision

We picked Avery NXR.

The deciding factors: → Cost was the lowest by clear margin → Output quality on our operational workflows was at par with cloud platforms → Data flow matched our compliance needs → Lock-in was structurally lower than alternatives → Maintenance burden was reasonable for what we got

We're using Avery NXR for the 4 workflows we evaluated, plus 6 more we've added since. Total cost for 30-person team: $10,440/year. Forecast pre-evaluation was 4-5x that for the same outcome.

What we'd tell other teams running this evaluation

Don't trust marketing pages. Run real workflows on real platforms with real data.

The marketing claims of every platform sound similar from the outside. The actual experience is very different.

Don't optimize only for setup time. It's seductive to pick the platform that took 60 minutes for first workflow vs. 90 minutes. Setup time becomes irrelevant after week one. Cost and quality matter for years.

Don't skip the cost projection at scale. Free tiers are misleading. Project costs at YOUR expected usage volume. The spread between platforms at 30-person team scale was 10x.

Don't underestimate data flow. If you have any regulated customers, sensitive data, or strict security policies, the question of where AI processes your data will become a blocker for cloud-only platforms eventually.

Try local-first at least once. Most teams skip this because the mental model is "AI = cloud." That model is wrong in 2026. The local-first option is real and may be the right answer for your operational workloads.

The bigger lesson

The right way to evaluate AI agent platforms in 2026 is the way you evaluated CRM in 2012 or ERP in 2002:

→ Map your real workflows → Test on real data → Project cost at real scale → Map data flow against compliance needs → Calculate lock-in cost → Make the call from numbers, not from marketing

Most teams skip this and pick based on Twitter sentiment, demos, or hand-waving. The teams that do the evaluation work make better decisions.

→ avery.software — Free Desktop tier. Run the evaluation. We're confident in how Avery shows up when measured.