AI agents and workflow automation

We build agents for workflows that are worth automating and consequential enough to need guardrails. State, approvals, and cost controls come first, so automation is dependable rather than impressive.

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A request or event starts an agent workflow with durable state, tool calls, human approval, and retries, which completes a logged action or escalates to a person.

What AI agents and workflow automation involves

An AI agent is a model that can take actions: look something up, call an API, update a record, send a message, then decide what to do next based on the result. That makes agents useful for multi-step work that used to need a person to move it along, such as research, triage, qualification, or back-office processing. It also makes them riskier than a chatbot, because a mistake is an action rather than a sentence.

We build agents as workflows with explicit structure, not as a single long prompt trusted to figure everything out. Each step has defined inputs, tools, and success conditions. State is stored durably, so a workflow can pause, wait for a person, survive a failure, and resume. Where an action is irreversible or expensive, a person approves it first, and every step is logged so you can see what ran and why.

Cost and reliability are designed in rather than tuned afterwards. Routine steps run on smaller, cheaper models, reasoning-heavy steps get stronger ones, and repeated work is cached. We wrote up how these decisions played out in our own product in How we kept LeadVector's inference costs predictable.

Problems we solve

A multi-step workflow is repetitive but too consequential to automate carelessly.
Early agent experiments are unreliable and hard to debug.
Autonomous actions need a human check before anything irreversible happens.

What's included

Agent architecture
Tool integrations
Workflow state
Human approval
Retry and fallback handling
Cost controls
Evaluation

How we approach it

01
Model
Map the workflow, its state, and the decisions that need a human.
02
Build
Implement agents with tool use, retries, and fallbacks.
03
Guard
Add approval gates, cost controls, and an audit trail.
04
Evaluate
Test against real cases and monitor reliability in production.

Decisions that matter

Narrow tools, clear permissions

An agent should only be able to do what the workflow needs. We give it small, well-described tools with scoped credentials, rather than broad access it might misuse.

Durable state and retries

Real workflows wait on people, external APIs, and slow systems. We persist state between steps and handle timeouts, retries, and fallbacks explicitly, so a failure in one step does not lose the work of the others.

Evaluate the workflow, not just the model

We test agents end to end against real cases, checking that the right tools were called, the right decisions made, and the final outcome correct, then keep monitoring those measures in production.

When it is the right fit

A good fit when

A multi-step process is repetitive, rule-heavy, and currently held together by people copying information between systems.
The work involves judgement over unstructured input, such as emails, documents, or research, that fixed-rule automation cannot handle.
You need automation with oversight: approval gates, an audit trail, and predictable cost.

Probably not when

Every step is fixed and deterministic. Conventional workflow automation is cheaper and more predictable than an agent.
The process has no tolerance for error and no point where a person can review it. Some decisions should stay with people.

Representative technologies

LangGraphWorkflow enginesTool/function callingQueuesProvider-agnostic model APIsObservability

Agents and Automation FAQs

How do you keep agents reliable?

Durable workflow state, explicit retries and fallbacks, and evaluation against real cases, rather than relying on a single long prompt.

Can a person approve actions before they happen?

Yes. We add human approval gates at the decisions that matter, with a full audit trail of what ran and why.

How do you control cost?

Routing, caching, and per-workflow budgets keep inference cost predictable even as usage grows.

What is the difference between an AI agent and a workflow automation tool?

Workflow tools follow fixed rules you define in advance. An agent can interpret unstructured input and choose its next step. We often combine the two: fixed steps where the logic is known, agent steps where judgement is needed.

Which systems can an agent work with?

Anything with an API or a reliable integration point: CRMs, ticketing, email, calendars, databases, and internal services. Where no API exists, it depends on whether a safe integration is possible.

Proof

Industry