Agentic Systems for Business Processes

AI agents can now own real work: reading intake emails, triaging tickets, extracting data from documents, reconciling records, drafting responses. Most of them never make it past the demo, because a system that is right 90% of the time will find its 10% in front of a customer. We build agents that reach production, by being deliberate about which decisions an LLM should make and which it never should.

Determinism Where It Counts, LLMs Where They Help

Every step in a business process falls into one of two categories.

Some steps have a correct answer. Calculating a refund, checking an order against inventory, looking up an account status, applying an approval policy. These belong in ordinary code: deterministic, testable, auditable, and free. Running them through a language model adds cost, latency, and a failure mode you didn't have before.

Other steps need judgment. What is this customer asking for? Which of these clauses deviates from our standard terms? Is this expense report describing the same trip as that calendar entry? Rules break down here because the inputs are unstructured, and this is where an LLM earns its place.

The architecture that connects the two is tool calling. The model decides what should happen and expresses it as a typed, schema-validated call. Your code decides how it happens, checks every argument, and executes against your systems. The model never writes directly to your database, never invents an account number, never sends an email on its own. It requests; deterministic code disposes.

What Separates a Production Agent from a Demo

An impressive demo needs a model and a prompt. A system your operations team can rely on needs four more things, and this is where most agent projects stall.

Guardrails That Act Before Execution

Limits on what the agent can do are enforced in code before any action runs, not logged after. Spending caps, allowed-recipient lists, rate limits, and scope boundaries are deterministic checks that no amount of prompt injection or model drift can talk their way past.

Tracing on Every Decision

Every model generation, tool call, validation result, and handoff is recorded. When the agent does something unexpected, you can replay the exact sequence and see which step went wrong, instead of guessing at a black box. Tracing is also how you monitor cost and latency per process, not just per API call.

Evals Instead of Vibes

Before an agent touches live work, we build an evaluation suite from your real historical cases: past tickets, past documents, past decisions with known correct outcomes. Every prompt change, model swap, or new tool runs against that suite first. You see accuracy as a number that moves, not as an impression from the last five outputs someone happened to read.

Human Review Where Consequences Live

Actions that are cheap to reverse can run autonomously. Actions that commit money, alter contracts, or reach customers get queued for one-click human approval. As eval scores and production history accumulate, you widen the autonomous zone deliberately, with data, instead of hoping.

Processes Worth Automating

The best candidates are high-volume, policy-driven processes where people currently do repetitive interpretation:

• Intake and triage: reading inbound email, forms, and tickets; classifying, enriching with account context, and routing to the right queue or straight to resolution
• Document processing: extracting structured data from invoices, contracts, and claims; flagging deviations from standard terms for human review
• Order and billing operations: matching purchase orders to invoices, investigating discrepancies, drafting dispute responses with the evidence attached
• Support escalation: resolving repetitive cases autonomously, assembling full context on the rest before a human ever opens the ticket
• Reporting and reconciliation: pulling data across systems, explaining variances, producing the weekly report a person currently assembles by hand
• Compliance checks: screening communications and transactions against policy, with every decision traced for audit

If your process isn't on this list, the discovery phase exists to find out whether it should be.

How We Work

Phase 1: Discovery and ROI Map (1-2 Weeks)

We sit with the people who run the process today and map every step: its inputs, its rules, its exceptions, its volume. Each step gets classified as deterministic, judgment, or human-only. You get an architecture proposal, an honest ROI estimate based on your volumes and labor costs, and a recommendation, which is sometimes that a plain workflow tool will do and you don't need us.

Phase 2: Pilot with Human Review (3-5 Weeks)

We build the agent for one process and run it against real cases with a human approving every output. This phase produces two things: a working system, and the evaluation suite built from what the humans corrected. The pilot ends with accuracy numbers, not anecdotes.

Phase 3: Production Hardening (4-8 Weeks)

Guardrails, tracing, monitoring and alerting, cost controls, failure handling, and the approval workflows for consequential actions. The agent moves from supervised to selectively autonomous, with the eval suite gating every change. This phase is where agent projects usually die when it's skipped up front; we scope it from day one.

Phase 4: Handover and Enablement (1-2 Weeks)

Your team learns to read traces, run evals, tune prompts, and extend the agent with new tools. We document the system and stay available, but the goal is that you own it. An agent your team can't operate is a liability with our name on it.

AlephZero Labs' Approach

We've spent years shipping ML systems where "usually works" wasn't acceptable, and we treat agents the same way. The interesting engineering in agentic systems is boundary design: deciding exactly how much freedom the model gets at each step, and building the scaffolding that makes the answer provable. That's the work we do, with handover designed so your team can keep the system understandable and operable.