AI Agent

Agents that act, not just chat.

Autonomous agents that reason through a goal, pick their own tools, and take real action across your systems — with human approval on the steps that matter.

Multi-Step Reasoning

Plans a route to the goal instead of following a script.

Real Tool Use

Reads and writes in your actual systems, not a sandbox.

Human In The Loop

Approval gates wherever an action is expensive or final.

Fully Observable

Every thought, tool call, and result recorded and replayable.

Scope an Agent → Watch One Think

ReasoningTool useMemoryGuardrails

Agent active — step 4 of 6

Reasoning Trace● thinking

Goal: resolve refund #4471—

Read order + payment history0.8s

Chose partial refund path1.4s

Awaiting approval — $240—

Blocked by policy until approved—

Anatomy

What Actually Makes Something An Agent

A chatbot answers. An agent decides, acts, checks the result, and adapts. These four parts are what separate the two.

STEP 01

The Planner

Breaks a goal into steps, decides the order, and revises the plan when a step returns something unexpected.

STEP 02

The Tools

Typed, permissioned functions into your real systems — read a record, issue a refund, book a slot, file a ticket.

STEP 03

The Memory

Context carried across the whole task and, where useful, across sessions, so the agent is not starting cold every time.

STEP 04

The Guardrails

Policy limits, approval thresholds, and hard stops that constrain what the agent is permitted to do on its own.

Reasoning layer

Reasoning

Planning that survives contact with reality.

The hard part of an agent is not the first plan — it is what happens when step three returns something the plan did not anticipate. We build agents that verify their own results, re-plan when reality disagrees, and know the difference between a recoverable error and a stop-and-ask.

Re-plans mid-task instead of failing on the first surprise
Verifies the result of each action before moving on
Distinguishes retryable failures from ones needing a human
Bounded step budgets so nothing loops indefinitely

Inside A Run

Watch An Agent Work Through A Real Task

This is a real trace shape from a support agent handling a refund request — every step it took, every tool it called, and the point where it stopped and asked a person.

agent-run · refund-4471● live

Goal received trigger

Customer emailed asking for a refund on a delayed order. Agent picked it up from the support queue.

goal: "resolve refund request" ticket: #4471 channel: email

Gathering context 3 tool calls

Pulled the order, the delivery record, and the customer's history before deciding anything.

orders.get(4471) → delayed 9 days shipping.trace(4471) → carrier fault customers.history(c_882) → 4 orders, 0 refunds

Reasoning to a decision plan

Full refund is not warranted — goods were delivered. Policy allows a shipping refund plus goodwill credit for carrier-fault delays over seven days.

policy.check(delay_carrier_fault) → refund shipping $40 + credit $200

Approval gate hit waiting

Total exceeds the $150 auto-approve threshold, so the agent stopped and routed it to a human with its full reasoning attached.

threshold: $150 requested: $240 status: awaiting human approval

Execute pending

On approval: issue the refund, apply the credit, and update the order record in one transaction.

Close the loop pending

Draft and send the customer reply, log the resolution, and close the ticket.

Approval requestedwaiting

The agent has done the work and stopped at the gate. A person sees exactly what it wants to do and why, then approves or overrides in one click.

Refund shipping$40.00

Goodwill credit$200.00

Update order record#4471

Thresholds are set per action type — refunds, sends, deletes, and writes can each have their own gate.
Approvals can route to Slack, email, or your own tooling rather than another dashboard to check.
Every decision, approval, and override is written to an immutable audit log.

Where Agents Earn Their Keep

Tasks Worth Handing Over

Agents pay off on work that needs judgement across several systems — not on single-step tasks, where plain automation is cheaper and more predictable.

Support Resolution

Reads the full history, decides the remedy, drafts the reply, and executes the refund or replacement once approved.

Support

Research & Enrichment

Gathers information across sources, cross-checks it, and writes structured findings into your CRM.

Sales

Document Review

Reads contracts and filings, extracts terms, flags deviations from your standard positions, and summarises the risk.

Legal ops

Order Exceptions

Handles the messy ones — partial stock, address failures, split shipments — deciding case by case.

Commerce

Data Reconciliation

Investigates mismatches across systems, works out which source is right, and proposes the correction.

Finance

Scheduling Negotiation

Works out a slot across calendars, constraints, and time zones, then books it and handles the rescheduling.

Ops

Incident Triage

First-line investigation on alerts — gathers logs, forms a hypothesis, and escalates with context attached.

Engineering

Onboarding Journeys

Provisions accounts, chases missing information, and adapts the sequence to what each customer actually needs.

Customer success

Analysis Requests

Answers open questions against your data, checks its own numbers, and shows the working alongside the answer.

Leadership

24/7Always-on execution

Agents work the queue overnight and at weekends without a shift roster.

100%Auditable actions

Every reasoning step and tool call is recorded and replayable.

50+Tool integrations

Across support, finance, commerce, data, and internal systems.

6-10wksTo production

From scoping to an agent running live against real work.

Tool surface

Tool Use

Scoped access to the systems that matter.

An agent is only as useful as what it is permitted to touch. Each tool is a typed, permissioned function with its own limits — the agent can read a customer record without being able to delete one, and issue a refund up to a ceiling without being able to empty an account.

Least-privilege scopes defined per tool, not per agent
Read and write separated so access can be granted gradually
Rate and value ceilings enforced outside the model
New tools added without retraining or rebuilding the agent

Chatbot Or Agent?

Knowing Which One You Actually Need

A useful distinction, because the wrong choice is expensive in both directions. Plenty of problems are better served by a good chatbot — or by plain automation with no model in the loop at all.

A Chatbot Is Enough When…

The job is answering, not doing.

The task is answering questions from known material
Actions are one step and low-stakes, if there are any
A person stays in the conversation throughout
Being wrong is embarrassing rather than costly
You need it live in weeks, not months

You Need An Agent When… agent

The job requires judgement and action.

The task spans several systems and several steps
The right next step depends on what the last one returned
Real actions get taken — money moves, records change
Cases vary enough that a fixed script keeps breaking
You need an audit trail of why, not just what

Guardrail architecture

Safety

Autonomy with a hand on the brake.

The question is never whether an agent will eventually try something it should not — it is what stops it when it does. Guardrails live outside the model, in code that the model cannot talk its way past, and they fail closed rather than open.

Value and rate ceilings enforced in the tool layer, not the prompt
Approval thresholds tuned per action type and per customer segment
Hard stops on destructive operations, always requiring a person
Instant kill switch and manual takeover on any running agent
Immutable audit log of every action, approval, and override

Multi-Step Reasoning

Agents that plan and execute multi-step tasks across your stack, without waiting on a human for every step.

Multi-step task planning and execution
Context retained across an entire workflow
Fails gracefully and reports what happened

24/7Always-on execution

Native Tool Integrations

Native integrations with your CRM, inbox, and internal tools — agents that actually take action, not just suggest one.

Native integrations with your existing stack
Read and write access, scoped to what's needed
Extensible to new tools as you add them

50+Integrations supported

Human-in-the-Loop

Approval gates for high-stakes actions, so autonomy never means losing control.

Configurable approval thresholds
Full audit trail on every action taken
Instant override and manual takeover

100%Auditable actions

Questions

What Teams Ask Before Handing Over Control

The concerns worth having. We would rather answer them now than in an incident review.

Guardrails sit outside the model, in the tool layer, where prompting cannot reach them. An agent that is not granted a delete tool cannot delete, regardless of what it concludes it should do. Value ceilings, rate limits, and approval thresholds are enforced in code, destructive operations always require a person, and there is a kill switch on every running agent.

It will sometimes, which is why the design assumes it. High-stakes actions sit behind approval gates. Actions that get through are reversible where the underlying system allows it. And every decision carries its reasoning trace, so a wrong call is diagnosable rather than mysterious — you can see which step went wrong and fix that specific thing.

No, and the difference is architectural rather than cosmetic. A chatbot produces text. An agent produces actions: it plans, calls typed functions against real systems, reads the results, and adapts. The comparison section above lays out which one your problem actually needs — sometimes it is the chatbot, and we will tell you so.

Six to ten weeks to production for a first agent, most of which is spent on the tool layer and guardrails rather than the reasoning. The model is the easy part. Defining what the agent may touch, under what limits, with what fallbacks, is the actual engineering.

Whichever fits the task, and we design so it can be swapped. Model capability is moving fast enough that locking your system to one provider is a liability. The tools, guardrails, and evaluation harness are ours; the model behind them is a component.

Evaluation sets built from your real cases, run on every change, so you can see whether a modification improved things or quietly broke an edge case. Plus production monitoring on approval rates, override rates, and task completion — a rising override rate is the earliest signal that something has drifted.

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