Hire AI employees that follow your SOPs and show their work.
Kognitos AI employees for finance and banking are AI agents, each hired into a role with a job description, the SOPs to do it, and clear limits. No SOPs written down? Quill, our builder agent, discovers how the work is done in your systems and writes each SOP in plain English your auditor can read. That same English is what runs, the same way every time: deterministically, on our patented English-as-Code execution engine. When it's unsure, it asks a person.
What is an AI employee?
An AI employee is an AI agent hired into a specific finance or banking role, such as AP Specialist or Reconciliation Accountant, with a job description, written SOPs, and limits on what it can do without approval. Unlike AI agents that decide each step as they go, a Kognitos AI employee runs the same SOP steps every time, shows what it did, and asks a person when it's unsure.
How is agentic AI used in finance?
Agentic AI in finance takes on repeatable work like invoice processing, reconciliation, cash application and the month-end close. The risk is agents that decide each step as they go, which auditors won't accept in production. Kognitos AI employees run approved SOPs instead, and every run has a journal an auditor can read.
2026 Sample Vendor, Gartner® Hype Cycle™ for AI in Finance & Agentic AI
#1 Overall and Exemplary Provider, 2026 ISG Buyers Guide for Automation
Hot Tech 2026, HFS Research, for deterministic AI automation
MIT Sloan Innovation Winner
Most Innovative AI Product, 2026 theCUBEd Awards
Gold Winner, Neuro-Symbolic AI Platform, 2026 Globee Awards
Other agents improvise.
Auditors reject them in production.
Most AI agents decide each step as they go. That's fine for drafting an email. It isn't fine for paying an invoice, closing the books or onboarding a customer, where you have to show why something happened and get the same answer tomorrow.
A typical AI agent
Decides each step at run time- 01Read the invoicemodel
- 02Decide what to checkmodel
- 03Skip the receipt check this timedrift
- 04Post to the ERPmodel
A Kognitos AI employee
Runs the SOP you approved- 01Read the invoicestep 1
- 02Build the fingerprintstep 2
- 03Detect exact and fuzzy duplicatesstep 3
- 04Block, hold or releasestep 6
Of enterprises will demote or decommission autonomous AI agents by 2027, because governance gaps surface only after production incidents.
Gartner prediction, May 2026Of AI agent deployment failures by 2030 will come from too little governance enforced at run time.
Gartner prediction, March 2026Models tested across 18 months of releases. Accuracy rose, but reliability barely moved: the most accurate agents still gave different results from one run to the next.
Rabanser, Kapoor et al., Towards a Science of AI Agent Reliability, 2026Hire it like a role.
Supervise it like a team member.
Pick a role
AP Specialist, Close Manager, KYC Analyst. Read its profile, including what it won't do and who it works with, and click Hire.
Discover and build its SOPs
Start from the library or from scratch. Quill, our builder agent, studies your systems and recent transactions, checks what it finds against your written policy, and drafts each SOP in plain English for you to approve. Cartographer maps your systems into the context graph it works from.
Set what needs approval
Choose, action by action, what it may do on its own and what needs a person first. Exceptions come to your team in plain English.
Read every run
Each run has a journal: every step in English, every system call, every exception and who resolved it, and the exact SOP version that ran.
61 roles, ready to hire
Every role runs a set of the 515 SOPs in the Kognitos library, split along separation-of-duties lines. Open one to see its SOPs and which are ready to run today.
Read the SOP.
Then read exactly what it did.
This is a real SOP from the Kognitos library, run by the AP Specialist. Quill compiles it once into code, and every step of the code maps back to its line of English. Hover a journal entry to see the step it came from, or a step to see what it did.
Overview
Screens an incoming supplier invoice against the AP ledger and recent invoice history to catch duplicates before they reach payment. It fingerprints the invoice on its identifying fields, runs exact and fuzzy duplicate detection against invoices already captured and paid in the ERP, diagnoses why a flagged candidate is or isn't a true duplicate, recommends a disposition, writes the hold or block back to the ERP, and records an evidence trail of the decision.
Inputs
| Input | Type | Default |
|---|---|---|
| invoice | file | — |
| company code | text | — |
| lookback window | decimal | 365 days |
| amount tolerance | decimal | 1% |
| fuzzy match threshold | decimal | 85 |
| auto-block confirmed duplicates | text | no |
| exception routing | text | AP exceptions queue |
Steps
- 1Open a duplicate-screening case for the invoice.
Create a tracked case for the invoice scoped to the company code, with its state set to
SCREENING. - 2Build the invoice fingerprint and pull the comparison set.
Resolve the supplier to its ERP vendor code, normalize the invoice number, and retrieve every invoice for that vendor posted or paid within the lookback window.
- 3Detect exact and fuzzy duplicate candidates.
Flag any invoice with the same vendor and normalized number. Score the rest on number, amount within the amount tolerance and date, and flag any at or above the fuzzy match threshold.
- 4Diagnose each flagged candidate.
Compare field by field and check its payment status. Tell a true duplicate apart from recurring charges, partial billings and credit memos.
- 5Recommend a disposition.
A true duplicate of a posted or paid invoice: block for payment, citing the original document number.
- 6Apply the disposition to the ERP and the case.
If auto-block confirmed duplicates is yes, post the payment block; otherwise stage it for an approver. Route anything needing review to the exception routing queue.
Outputs
- A screening verdict with the match type and the original document number it duplicates.
- A diagnosis and recommended disposition, with the reason.
- A payment block posted to the invoice in the ERP, or a staged block for approval.
- A screening report and a notification to the AP team.
- step 1Opened case DUP-7781company 1000 · state SCREENING
- step 2Fingerprint built, 214 invoices to comparevendor 100482 · ACME20931 · $48,212.50 · Sep 22
- step 31 exact match found5105600112 · same vendor + number · EXACT
- step 4Diagnosed TRUE_DUPLICATEamount, PO and lines identical · original paid Sep 28
- step 5Recommended: block for paymentcites original document 5105600112
- step 6Auto-block is off: staged for approvalapproved by [J. Rivera] · 10:42
- step 6Payment block posted, case confirmederp.post_payment_block · email → AP team
The most advanced neurosymbolic AI employees
They don't hallucinate when they execute a standard operating procedure. Here's exactly where the AI does its thinking, and where it doesn't. What is neurosymbolic AI?.
Quill discovers and writes the SOP
Describe the work, start from the library, or let Quill find it: it studies your systems and how recent transactions were handled. Then it writes the SOP in English, compiles it into code, and tests it on your real cases.
- You review the SOP and the graph before they're published.
- Every change becomes a new version.
It chooses which SOP to run. The SOP runs deterministically.
- It can only run SOPs you've published and turned on for it.
- You decide which actions need approval first.
It reads what people write
Steps like reading an invoice PDF or a customer email use models. They're marked in the SOP, and their results are checked before the next step runs.
- Low-confidence fields go to a person.
- What it read is kept with the run.
When it's unsure, it asks. Then it remembers, with your approval.
It stops instead of guessing. The run pauses at the exact step, with the data in front of it, and explains the problem in plain English.
Your team answers. Fix a value, answer a question, retry, skip or stop. The run continues from where it paused.
Fixes are reviewed before they're reused. If the answer should apply next time, it becomes guidance, applied only after an approver signs it off.
It learns what your systems don't know, and shows you what it learned.
Every AI employee comes equipped with a Kognitos context graph, an industry-leading living map of your vendors, bills, payments and accounts, built by Cartographer from the apps you already use. It captures the tribal knowledge that never makes it into a system of record, so your AI employees gain autonomy over time while their operating behavior stays auditable and editable.
Same tax ID. "Supply" is the remit-to name Ramp uses for Acme.
Tribal knowledge, captured
Which two vendor records are one company, which payment settles which bill, which ERP field is simply wrong. People add it to the graph's knowledge layer with a reason; Cartographer proposes links it infers, with the evidence, and a person accepts or rejects each one. Your source systems are never changed.
Every judgment call, on record
When someone clears or confirms a finding, the decision trace keeps who, when and why, with the data as it was that day. When the same issue comes back, last time's reason is right beside it.
Behavior you can audit and edit
Everything an AI employee has learned is a reviewable item: an approved piece of guidance, a knowledge-layer entry, a decision trace. Each one shows who added it and why, and any of it can be edited or retracted, with the change versioned.
Fewer runs need a person, because it keeps what your team teaches it.
Every approved piece of guidance and every accepted fact in the graph is one less question next time. You decide when an action moves from "ask first" to "act", and that change is versioned too.
Built for work you have to defend
| Kognitos AI employees | Other AI agents | Suite agents (ERP, CRM) | RPA | |
|---|---|---|---|---|
| What runs | Your SOP in English, compiled to code | A model choosing each next step | A model inside one vendor's system | Scripts and selectors |
| Same input, same steps | Yes, deterministically | Not guaranteed | Not guaranteed | Yes |
| Who can read the logic | Your controller and your auditor | Engineers | Admins | Developers |
| Audit record | A journal of each step, mapped to the SOP | Traces and tool logs | Platform logs | Run logs |
| Unexpected cases | Pause, ask a person, learn with approval | Retries or a coded pause | Hand off to a person | Fail |
| Tribal knowledge | Captured in a context graph, with who added it and why | Prompts and chat memory | Inside one system | Not captured |
| Documents and browsers | Built in, both run as code | Build it yourself | Inside the suite | Separate products |
| Who maintains it | Your team, in English | Your engineers | The vendor or an SI | An RPA team |
Built on English-as-Code, which we invented
Kognitos has been building software that runs plain English since 2020. Two US patents are granted, and more are pending.
Natural language programming of a new function or skill
Teach a running program a new skill in plain English, without restarting it.
Read the patent ↗Coding interactively without having to restart a procedure
Undo a step in a running procedure and continue from there, using versioned memory.
Read the patent ↗Learning, replay and regression
Learning from exceptions, editing a past run and resuming, checking new results against past runs, and building tests from history.
What finance and risk teams ask us
What is an AI employee?
An AI employee is an AI agent hired into a specific finance or banking role, such as AP Specialist or Reconciliation Accountant, with a job description, written SOPs, and limits on what it can do without approval. Unlike AI agents that decide each step as they go, a Kognitos AI employee runs the same SOP steps every time, shows what it did, and asks a person when it's unsure.
How are AI employees different from AI agents for finance?
Most AI agents for finance decide each step as they go, so the same invoice can be handled differently from one run to the next. An AI employee runs the SOP you approved, step by step, the same way every time. Every run has a journal: each step in English, every system call, and the exact SOP version that ran. When a case doesn't fit the SOP, it stops and asks a person instead of improvising. See how agentic AI platforms for finance compare.
Which finance and banking processes can AI employees automate?
Each role opens its profile, with its SOPs and what it won't do.
Finance
- AP and invoice processing: AP Specialist (see how to automate accounts payable and 3-way match automation)
- Reconciliation: Reconciliation Accountant (see AI platforms for automated reconciliation and automated reconciliation software)
- Cash application and collections: Cash Application Specialist (see AI tools for cash application) and Collections Specialist
- Month-end close: Close Manager (see the zero-headcount close)
- Intercompany: Intercompany Accountant (see intercompany reconciliation)
- Treasury: Cash Manager
- Tax: Tax Provision Accountant and Indirect Tax Analyst
- Payroll: Payroll Specialist
- Internal controls and SOX: Internal Controls Analyst (see SOX evidence collection)
Banking
- KYC and onboarding: KYC & Onboarding Analyst (see KYC automation and KYC and customer due diligence)
- Loan processing: Loan Processor & Closer
- Card disputes and chargebacks: Card Disputes Analyst
- Payments: Payments Operations Specialist
- Trade finance: Trade Finance Operations Specialist
Banking SOPs are in the library and still to be built with your team.
Is an AI employee replacing someone on my team?
It takes on defined, repeatable work so your team has capacity for judgment calls, exceptions and analysis. Each role lists the people it works with, and people stay accountable for approvals.
What if our SOPs aren't written down?
Most teams' aren't, and you don't need to map them first. Quill, the Kognitos builder agent, investigates your connected systems: which tables and fields matter, how they relate, and how recent transactions were settled. It checks what it finds against your written policy, drafts the SOP in plain English, tests it on your real cases, and asks a specific question only when something essential is unclear. You approve the SOP before it runs, and exceptions keep teaching it after that.
What does "neurosymbolic" mean here?
Language models (the neural part) understand requests, read documents and help Quill write SOPs. The English-as-Code engine (the symbolic part) executes each SOP step exactly as written. The model chooses which SOP to run; it never decides what the SOP does.
What does the context graph do for an AI employee?
It gives the employee the business context its SOPs work on: your vendors, bills, payments and accounts, how they connect across systems, and what your team knows that the systems don't. When the employee learns something, it's proposed to the graph and a person accepts it, so what it knows is always reviewable. Every decision on a finding is kept with its reason and the data as it was that day. Learn about Context Graphs.
Can it approve its own work or release a payment on its own?
Roles are split along separation-of-duties lines, and you choose for each action whether it may act or must ask first. Payment release and journal approval stay with a person unless you decide otherwise.
How do I audit what it did?
Each run has a journal: the request, each step in English, the inputs and outputs of every system call, any exceptions and who resolved them, and the exact SOP version that ran.
Which systems does it work with?
ERPs, banks, document stores and collaboration tools through Kognitos integrations [list and count to confirm], plus browser automation for systems without an API. Your team can add integrations with the Kognitos Book Development Kit.
Is my data safe?
Kognitos is SOC 2 Type II and ISO 27001 certified and HIPAA compliant, runs on AWS with EU and APAC residency options, and doesn't train shared models on your data.
Make your first hire.
Start with one role. Quill discovers and builds its SOPs with your team, on your real cases.
Benchmarks and analyst figures are cited from public sources; customer metrics and logos will be added once approved. The run shown is illustrative.