Aelix Echo · AI Agents

>observing operational state...
>planning intervention...
>ready.

Agents that take
work off your desk.

Software with a goal, a set of tools, and the judgment to use them. Aelix Echo builds, deploys, and operates AI agents that observe, reason, act, and hand off, inside the systems your business already runs on.

What an agent is

An agent is software with a goal, a set of tools, and the judgment to use them.

Not a chatbot. Not a script. An agent observes the state of the work, plans a sequence of actions, calls the systems that can move that work forward, watches what happens, and decides whether to keep going, change course, or hand off to a person. We build the boring parts that make that loop trustworthy.

How an agent thinks

01
Observe

It starts by knowing the state of the work.

Agents read from the systems that hold the truth: ticketing, EHR, CRM, accounting, mainframe, document store. We wire the observation layer so the agent never argues with reality.

02
Plan

It picks an order of operations, and a fallback for each.

Goal decomposition, tool selection, retry budgets. The plan is a first-class artifact the agent can revise, log, and explain, not a chain hidden inside a prompt.

03
Act

It calls the systems your business already runs on.

Salesforce, Stripe, SAP, Workday, custom internal APIs. Every tool call is scoped, rate-limited, logged, and reversible. The agent can do everything a junior operator can, with the constraints you'd give a junior operator.

04
Verify

It checks its own work before saying it's done.

Output validation, confidence thresholds, and a second-pass review for the calls that matter. An agent that doesn't verify is a bot, and a bot that doesn't verify is an outage.

05
Hand off

It knows when to ask a human, and what to ask.

Exception queues with the full reasoning trace, the proposed action, and the confidence behind it. The human resolves the call in seconds, because the agent already did the homework.

A glimpse of the work

aelix.app · agents · refund-handler · run #4827

Goal · process refund for ATX-44102

Running
Tools available · salesforce, stripe, email · scope · single order
ThoughtCustomer asks for refund on order ATX-44102. Need to verify the order is eligible, then process and confirm.
Actionsalesforce.lookuporder: ATX-44102
ObservationOrder found · paid 4d ago · eligible window: 30d · status: active
ThoughtEligible. Refunding to the original payment method and emailing confirmation.
Actionstripe.refundamount: $148.20 · method: original
ObservationRefund completed · txn re_3PqXYZ · settled to card ending 4242
Actionemail.sendtemplate: refund_confirmation
ObservationEmail sent · message-id 5e72…
DoneCompleted in 8.4s · 4 tool calls · $0.04
Trace · 4 actions · 3 observations · 0 escalations
Sealed for audit

Live ReAct trace · refund handler agent

Outcomes

75-90%

Tasks resolved without escalation

Under 10s

Median first action

$0.02-0.10

Per-task cost, end to end

Aelix Echo internal engagement benchmarks

How we build them

A tight scope, observed runs, and the patience to tune the edges.

01

Define the intent

What the agent is responsible for, what it must never do, and what counts as done. Written before a single tool is wired.

02

Wire the tools

API contracts, auth scopes, rate limits, rollback paths. The agent gets the tools a junior operator would get, and the supervision a junior operator would get.

03

Deploy and observe

Behind a feature flag, with full tracing on every run. We watch the first hundred runs together, not over email.

04

Tune the edges

Escalation thresholds, prompt and tool revisions, regression tests on real traces. The agent gets quietly better while the team gets visibly faster.

A note from the practice

An agent without scope is a liability. An agent without observability is a black box. We build neither. Every run is bounded, traced, reversible, and explainable, because the day an agent surprises you is the day the program ends.

The Aelix Echo agents practice

When the work doesn't need a meeting, just an answer, we're here.

45-minute working session · One real task, agent-scoped live