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Enterprise AI agents

Enterprise AI agents, orchestrated across every business function

306 agents already running in production across 13 business functions — plus the engineering to adapt any of them to your systems, your tolerances and your approval chain.

  • 306 agents live
  • 13 business functions
  • 93 processes
  • Runtime workflow published
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Business systems

  • Google Drive Cloud storage NDA_Acme.pdf
  • Google Sheets Spreadsheets pipeline.xlsx
  • Google Docs Documents SOW-1182.docx
  • Box Content cloud inv_0412.pdf

Read-only · scoped to your permissions 12,480 documents in scope

Business tools

  • HubSpot CRM OPP-48-1120
  • Zendesk Ticketing TCK-4821
  • SAP ERP PO-99312
  • Snowflake Warehouse SKU 77-B

Read and write · every write needs approval 4 systems · 9 record types

Public data

  • Google Web search acme renewal
  • Crunchbase Company data Co. 08213445
  • Google News News Rate notices
  • Wikidata Reference Tariff 2026

Read-only · cached with its source URL Refreshed every 6 hours

Agent Runtime

NDA Analyzer Legal

NDA_Acme_v4.pdf · 12 pages · read 2 min ago

  • 41Clauses
  • 34Standard
  • 7Flagged
  • Term length 83% match
  • Carve-outs 58% match

Box Filed to the review folder

Lead Qualification Sales

OPP-48-1120 · inbound demo request

Lead score

Acme Ltd · enterprise · 1,200 seats

85
  • Fit34
  • Intent28
  • Budget23
Routed to an account executive

HubSpot Opportunity owner updated

Invoice Adjustment Request Billing

TCK-4821 · March invoice · rate change

“Your March invoice was charged at the old rate. A corrected invoice and a credit note for the difference are attached.”

Billing policy · correction inside the 90-day window

Held for billing approval · nothing sent

Zendesk Draft saved on the ticket

NDA Analyzer, Lead Qualification and Invoice Adjustment Request are real entries in the agent catalogue. The systems named are examples of what an agent can read and write; each mark is a trademark of its owner. File names, record IDs and figures are illustrative — not customer data.

What is an enterprise AI agent?

An enterprise AI agent takes actions inside your business systems — reading a ticket, matching an invoice, updating a CRM record — rather than only producing text about them. What separates one that survives production from one that demos well is everything around the model: retrieval scoped to your own documents and permissions, guardrails enforced in code rather than in prompt instructions, a named human approving anything irreversible, and an audit trail that reconstructs every action back to the source it came from. Leverge runs 306 such agents across 13 business functions and 93 processes, and publishes the runtime workflow for each one.

Runtime

How an enterprise AI agent actually runs

The same six stages every agent on this site executes, in order. Most vendor diagrams stop at the arrow labelled 'AI' — the interesting part of this one is stage five, which is where a person stays in the loop and why an agent can be trusted with a production system at all.

  1. Trigger

    A ticket lands, a schedule fires, or someone runs it by hand. The agent starts from an event in a system you already own.

  2. Retrieve

    It gathers the governing policy, the customer record and the history — scoped to what the requesting user is allowed to see.

  3. Decide

    It plans the steps against your written rules, not against whatever the model finds most plausible in the moment.

  4. Check

    Every action is classified by blast radius. The limits are enforced in code, outside the model, where no instruction can argue past them.

  5. Approve

    Anything irreversible waits for a named person, who sees the evidence already assembled rather than a request to trust the output.

  6. Act and log

    It writes to your CRM, ERP or ticketing system, recording the trigger, the sources, the rule applied and who released it.

Every correction feeds the evaluation set

Capabilities

The features that decide whether agents work at your scale

Almost any agent demos well against a clean example. These are the properties that separate one still running in month nine from one quietly switched off in month two.

System adaptability

Runs inside the stack you already have

Agents connect to the CRM, ERP, ticketing and document systems in place today. No second system of record, and no migration standing between you and the first working agent.

Grounded in your own data

Retrieval is scoped to your documents, your policies and the specific record in front of it, with existing permissions honoured per user rather than flattened into one index.

Model and cloud agnostic

Which model runs a task is a configuration value, chosen by measuring candidates against your evaluation set. Deploy to AWS, Azure, GCP or your own tenancy.

A catalogue of 306, or a new one

Start from an agent already doing the job somewhere and diverge from it, or have one built for a process no catalogue covers. Most deployments are the former.

Intelligence and automation

Workflows you can read before you trust them

The logic governing an agent is a published workflow — every trigger, every action, every checkpoint — rather than an opaque chain of prompts you can only evaluate by watching it run.

End to end, but not unattended

An agent carries a task from trigger to completion and stops only where a decision genuinely needs a person, instead of pausing at every step and calling that human oversight.

Improves from corrections, not from vibes

Every human override is captured and added to the evaluation set. The next release is measurably better against your own historical cases, or it does not ship.

Agents that hand work to each other

A qualified lead moves from the sales agent to the proposal agent with its context intact, under one orchestrator that owns the sequence and the failure handling.

Benefits

What changes for the enterprise

Grouped by who asks. Risk owners read the first column, IT the second, the operations lead the third, and whoever signs the budget reads the fourth.

Reliability

  • Guardrails in code, not in prompts

    Every action is classified by blast radius and gated outside the model. A prompt instruction is the weakest control available and is never the only one in place.

  • An audit trail that survives review

    Each action records its trigger, inputs, retrieved sources, the tolerance applied and the person who released it. That chain is what makes automation defensible after the fact.

  • Measured before it ships

    Agents are scored against an evaluation set built from your real historical cases — assembled before any agent logic is written, so it cannot be tuned to flatter the build.

Adaptability

  • Fits the systems you already run

    Integration goes through your existing APIs and record structures. Nothing asks a team to change how it works before the agent becomes useful to them.

  • Deployed where the data is allowed to live

    Public cloud, private cloud or your own tenancy — chosen by your data residency and retention position rather than by whatever is quickest to stand up.

  • One process at a time

    Agents are deployed and versioned independently, so adding the eighth cannot destabilise the first seven, and a rollback is one agent rather than a platform.

Automation

  • The queue stops being the constraint

    Inbound work is triaged, enriched and prepared continuously, so a shift starts with decisions waiting rather than with an hour of sorting to find them.

  • Exceptions arrive already explained

    When an agent cannot complete a task it hands over the discrepancy, the evidence and the likely cause. Gathering that is the twenty minutes; deciding is the two.

  • Accuracy on the work people rate worst

    Matching, keying and reconciliation are high-volume and low-judgement — the exact profile where a measured system beats a tired person on a Friday afternoon.

Business gains

  • Cycle times measured in minutes

    The delay in most enterprise processes is waiting for someone to be free, not the work itself. Removing the wait is where the visible improvement comes from.

  • Cost out of the process, not the people

    The saving is in the hours currently spent on retrieval, keying and chasing. Those hours move to work that needs judgement, which is the part you were short of.

  • A position you can defend to a regulator

    Published workflows, enforced guardrails and a complete audit trail are what let you scale automation into regulated processes instead of stopping at the safe ones.

Implementation

Getting an agent into production

Three stages, roughly six weeks to the first live agent. The timeline is usually set by system access and evaluation data rather than by build time.

  1. Choose the agent

    Week 1

    Pick from the 306 agents already running, or scope a new one against a process no catalogue covers. Most engagements start from a catalogue agent that is close and diverge from there — different systems, different tolerances, a different approval path.

    You receive: A named process, the agent it starts from, and a written list of what has to change.

  2. Shape the workflow

    Weeks 2-4

    Define the decision path, the tolerances and which actions require a named approver. This is the step where your policy stops being prose in a handbook and becomes configuration the runtime enforces.

    You receive: The published runtime workflow, the guardrail set, and an evaluation set built from your historical cases.

  3. Deploy and watch it

    Week 5 onward

    Ship behind your existing approvals, then track resolution rate next to quality and reopen rate. An agent measured on throughput alone will optimise for throughput, and that is how automation quietly costs you customers.

    You receive: A live agent, a dashboard covering all three metrics, and a monthly review of what it got wrong.

Book a technical call How we build agents

Explore our agents

306 agents across 13 business functions

Every agent below is live, not a roadmap item. Pick one that already does the job and adapt it, or have one built for a process none of these covers. 93 processes are covered today.

Written in depth

Longer pages on the four functions we are asked about most, each with the full runtime workflow, the failure modes and what the evaluation set has to contain.

Agent coverage at a glance

Agents live in production today
306
Business functions covered
13
Distinct processes automated
93
Irreversible actions taken without a named approver
0

Enterprise AI agents: common questions

What is the difference between an AI agent and a chatbot?
A chatbot produces text. An AI agent takes actions in your systems — it updates a CRM record, posts a journal entry, routes a ticket, or refuses to and escalates instead. That difference is why the engineering around an agent is mostly not about the model: an incorrect sentence is an inconvenience, while an incorrect write to a production system is an incident, so the work goes into retrieval scoping, guardrails and approval gates rather than into prompting.
How do you stop an enterprise AI agent from doing something irreversible?
Every action an agent can take is classified by blast radius before the agent is built. Reversible, low-impact actions run autonomously. Anything irreversible — a payment, an external message, a deletion, a permission change — is gated in code outside the model and waits for a named human approver. The gate is a configured rule rather than a prompt instruction, because a prompt is the weakest control available and can be argued past by the content the agent is reading.
Do we have to replace our existing systems to use AI agents?
No. Agents connect to the CRM, ERP, ticketing and document systems already in place, through the APIs those systems already expose. There is no second system of record and no migration prerequisite. The common exception is data quality rather than architecture: if a process depends on a field nobody has filled in reliably for two years, that has to be fixed regardless of whether an agent or a person is doing the work.
How long does it take to get the first agent into production?
Around four to six weeks for an agent starting from an existing catalogue entry, and longer for a process with no template. The schedule is usually set by access and evaluation rather than by build time — getting credentials into the right systems and assembling an evaluation set from real historical cases takes longer than writing the workflow. Engagements that skip the evaluation set ship faster and then have no way to tell whether the agent is working.
How do you measure whether an AI agent is actually working?
Against an evaluation set built from your own historical cases before any agent logic is written, and then in production against at least three metrics rather than one. Resolution or containment rate on its own is misleading: an agent that closes work badly scores excellently on it. Tracking quality and reopen or reversal rate alongside it is what surfaces the failure mode where throughput improves and outcomes get worse.
Can AI agents work with regulated or confidential data?
Yes, provided deployment follows the data rather than the other way round. Agents can run in your own cloud tenancy or a private deployment so records never leave your control, retrieval honours the permissions each user already has instead of flattening them into a single index, and every action is logged with the sources it read. In regulated processes the binding constraint is almost always auditability rather than accuracy, which is why the audit trail is built first.

Next Step

Transform the processes you already run, not the ones in a slide deck

A 30-minute technical call with an engineer who has shipped this before. Bring the process you want automated and you will leave with a feasibility read, the agent it should start from, and an honest answer about whether it is worth doing at all.

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