Caddi vs. Humanity LabsHumanity Labs reasons through every step on every run. Caddi uses AI only where it's needed and runs the rest as code.
Seven steps at 90% comes out clean less than half the time; code comes out the same on run 4,000 as on run 1. Your team owns the automations and can open any run to see every step.
- 99%of agent runs complete successfully
- ~45 minon average to build your first agent
- 1M+back-office tasks completed
Jason AlafganiUpdated Sep 23, 2026
Seven steps at 90% is right less than half the time.
When a model reasons through every step, the odds multiply: 90% right, seven steps in a row, comes out clean 48% of the time.
A managed AI workforce that reasons through every step, on every run.
The steps you already know run as code, the same way on run 4,000 as on run 1. AI handles only the steps that need judgment.
A finished task isn't proof it was done right.
Compliance has to trace exactly what happened on each account. A summary of outcomes can't show that.
Delivers finished work, with outcome and SLA reporting from the vendor.
Every step of every run is logged and reviewable by your own team, so the record is evidence of the process, not a report about it.
Trust you can't check is trust you're asked for.
When the workforce belongs to the vendor, so do the answers to why something happened.
The vendor runs the workforce and reports back to you.
Your ops team owns the automations, opens any run to see each step, and changes the process in plain English when the work changes.
Frequently asked questions
Is Caddi an alternative to Humanity Labs?
Yes, for the same operational work, but built on a different architecture. Humanity Labs sells a fully managed AI workforce for wealth managers and RIAs, where agents reason through tasks on every run. Caddi runs on hybrid agents: the workflow executes as deterministic code, AI is called only for the bounded judgment steps that need it, and a separate improvement loop refines the automation between runs. Caddi also starts earlier: Discover finds and ranks the repetitive work in your own systems, so you don't have to scope the engagement yourself. That makes Caddi the better fit for work you want done a certain way, reliably, and a reasoning-first workforce the better fit for work where even you don't know the right way yet.
Why is a hybrid agent more reliable than a pure AI workforce?
Because non-determinism compounds. When a model decides every step, each step is a fresh probabilistic call and the odds multiply: a step that is right 90% of the time comes out clean only 48% of the time across seven steps, so ninety percent right seven times in a row is mostly wrong. Benchmarks bear it out. On TheAgentCompany, a Carnegie Mellon benchmark of real professional tasks, the best frontier models, Claude among them, completed about 30% of tasks on their own and scored under 40% even with partial credit. A hybrid agent runs the fixed steps as code, which does the same thing every time, and keeps AI to the steps that need it, each one scoped, logged, and reviewable.
What is a hybrid agent, and why does it matter at high volume?
A hybrid agent does the work with deterministic code, uses AI for the real judgment calls inside a run, and improves its own automation between runs rather than during them. It matters at volume because putting a model in the driver's seat on every step scales badly in three ways: cost tracks the number of runs, behavior varies run to run, and latency compounds across steps. Deterministic execution keeps the marginal cost of another run close to the cost of running code, produces identical output for identical input, and clears a queue immediately over APIs.
Which is more reliable for reconciliation, billing, and compliance work?
For work where the numbers have to tie every time, deterministic execution is the safer default. Caddi runs production as code, so the same inputs produce the same outputs on every run and every step is logged. That turns the audit trail into evidence of a process rather than a transcript of one attempt. Agent architectures that reason on each run are more flexible on messy, novel cases, but their output varies between runs, so it needs oversight before you rely on it.
Is a pure AI workforce ever the better choice?
Yes: when even you don't know how the work should be done. A nonstandard trust instrument that needs a call made, an M&A diligence file where no two documents match, a request nobody at the firm has handled before. There is no right way to write down yet, so an agent that reasons through each case with a human reviewing is the right shape. The line isn't judgment, since Caddi uses AI for judgment calls inside every run. It's whether you know how you want the work done. If you do, you want it done that way every time, and that is Caddi.
Does Caddi help figure out what to automate?
Yes. Discover connects read-only to the tools your team already uses, finds the repetitive work running across them, and ranks it by estimated hours, so the first workflow you automate is chosen from evidence. With a managed AI workforce, your team scopes which functions to hand over, which assumes you already know where the hours go.
Does Humanity Labs have deeper RIA expertise than Caddi?
No. Humanity Labs focuses only on wealth management, but Caddi has been automating RIA operations since before Humanity Labs was founded: custodian paperwork, account opening, reconciliation, billing, and CRM upkeep. The difference is that Caddi also runs the same platform for law firms, private equity, banks, insurance, and accounting.
Does Caddi still use AI, or is it just scripts?
Caddi uses AI in two places, just not in the hot path deciding what to do next. Inside a run, AI handles the judgment steps: classifying an ambiguous email, extracting a field from a document that never looks the same twice, deciding which reconciliation break deserves a human. Between runs, a background improvement loop re-evaluates the automation for brittleness and new edge cases and proposes better code. The deterministic part is what keeps every run cheap, fast, and identical.
Do you have to sign a multi-year contract to get started with Caddi?
No. Caddi starts with one workflow, ideally your highest-volume one. You show it the task on a screen share and it goes live immediately, so you see a result before you scale. A managed AI workforce is scoped differently by nature: capacity sized in full-time-equivalents across a function, onboarded and trained before it produces, which is a program rather than a pilot. That is the right shape for a firm committing to a new operating model, and the wrong shape for a firm that wants to test the idea this quarter.