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Autonomous and semi-autonomous agents that research, decide and act across your tools — with the orchestration, guardrails and audit trails to trust them.

AI Agents at WEBX

The problem

Knowledge work is full of robot work

Your best people spend hours on triage, research, data entry and coordination — work that follows patterns a well-built agent can execute reliably.

Support tickets triaged by hand, one at a time

Research and reporting that consumes entire days

Processes stalling overnight waiting for a human click

Naive agent experiments that go off the rails without oversight

AI Agents solution in practice

Our solution

Agents with judgment and boundaries

We build agent systems on LangGraph and Temporal: multi-step reasoning, tool use across your stack, human approval gates where stakes are high, and full audit logs.

Multi-agent orchestration with explicit state machines

Tool integrations: CRM, email, databases, internal APIs

Human-in-the-loop checkpoints for consequential actions

Durable execution that survives failures and restarts

Benefits

What this unlocks for you

Around-the-clock throughput

Agents process queues at 3am with the same care as 3pm — no backlog mornings.

Consistent quality

Every case handled by the same playbook, with escalation when confidence drops.

Full auditability

Every decision, tool call and data access logged and replayable.

Humans on the interesting work

Your team supervises outcomes and handles exceptions instead of grinding queues.

Technology

The stack behind the work

Chosen for reliability today and headroom tomorrow.

TypeScript
LangGraph
Temporal
Anthropic
OpenAI
Claude
PostgreSQL
Redis

Our process

From first call to production

1

Workflow mapping

We shadow the real process — inputs, decisions, exceptions — before automating any of it.

2

Agent architecture

State machines, tool permissions and escalation rules designed for your risk profile.

3

Sandboxed pilot

The agent runs in shadow mode against real cases while humans verify every output.

4

Graduated autonomy

Approval gates loosen as measured accuracy earns trust, category by category.

5

Scale & monitor

Dashboards for throughput, accuracy and cost; continuous tuning as your business evolves.

Case study: SaaS support organization

Case study — B2B software

SaaS support organization

Challenge

A 40-person support team faced 6-hour first-response times and burned-out senior agents doing repetitive triage across 3,000 weekly tickets.

Solution

A triage-and-resolve agent system: classification, knowledge-grounded draft responses, autonomous handling of six safe categories, and human review gates for the rest.

58%

tickets resolved autonomously

6h → 4min

first response time

+23

NPS improvement in two quarters

FAQ

Common questions

Layered controls: strict tool permissions, spending and rate limits, human approval gates for consequential actions, and kill switches. Autonomy is granted per action type, based on measured accuracy — never all at once.

Ready to talk about ai agents?

One call is enough to know whether we're the right team. No pitch decks — just engineers who ask good questions.