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AI AGENT DEVELOPMENT SERVICES

Build AI Agents That Take Work Off Your Team

Tech.us builds AI agents that reduce hours of manual work, resolve cases faster, and free teams to focus on higher-value work.
We design the tools, permissions, approvals, and failure handling that let agents act safely inside your business systems.

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Trusted by organizations that depend on technology

WHAT AI AGENTS CHANGE

Remove Manual Work Without Removing Human Control

AI agents can take on repeatable work across systems so teams spend less time moving information and more time on work that needs human judgment.

Reduce Hours of Manual Work

Let agents handle repetitive steps that currently consume employee time across everyday workflows.

Resolve Work Faster

Move cases and requests forward without waiting for every routine step to be completed manually.

Redirect Team Capacity

Shift employee time toward higher-value work instead of adding more people to handle growing operational volume.

Increase Processing Capacity

Handle more work without increasing manual effort at the same rate.

AI AGENT DEVELOPMENT SERVICES

Build Agents That Can Complete Real Business Work

We design and deploy AI agents that connect to your existing systems, take approved actions, and move multi-step work forward with the right level of human oversight.

Workflow and Feasibility Assessment

We examine a candidate process, establish whether an agent is warranted rather than deterministic automation, and define the outcome that counts as completion.

Tool and Action Layer Engineering

We design and build the tool interfaces the agent operates through, including validation, idempotency, and error semantics.

System Integration

We connect agents to CRM, ERP, ticketing, finance, document, and custom systems through interfaces built for machine use rather than screen automation.

Permission and Identity Design

We define what each agent may reach, whose authority it acts under, and which actions require confirmation before execution.

Knowledge Access for Agents

We give agents grounded access to the material a decision depends on, scoped to what the task and the requesting user are entitled to see.

Multi-Agent Coordination

Where one agent cannot hold a broad process, we build specialized agents with defined responsibilities and explicit handoffs between them.

Failure and Recovery Engineering

We design what happens on partial completion, timeout, unavailable systems, and ambiguous state, including compensating actions where a step must be undone.

Agent Evaluation

We build evaluation over complete runs rather than single responses, covering tool selection, path taken, and behavior under induced failure.

Cost and Step Control

We bound reasoning loops, tool call volume, and context growth so an agent's operating cost stays predictable as usage rises.

Deployment and Monitoring

We deploy agents into production and instrument completion rates, tool failures, escalations, cost per run, and the paths taken.

Selected Work

Work That Creates Real Business Value

Explore how we help organizations solve complex challenges and improve how their business operates.

View All Case Studies

AI-Powered Takeoffs From Complex Construction Drawings

A precast concrete manufacturer partnered with Tech.us to bring AI into the estimating workflow. The system uses OCR, computer vision, and generative AI to identify, extract, and visualize structures, pipes, and components from complex construction drawings.

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Wellington Hamrick Precast AI takeoff automation tool shown on laptop and tablet

Mobile Fleet Visibility Built for Field Operations

SkyHawk by TELUS needed a streamlined mobile experience for its Connect Anywhere platform. Tech.us built the core experience around real-time asset tracking, fleet activity, secure configuration, and map-based operational visibility.

Read the Case Study
SkyHawk by TELUS mobile app screens showing fleet tracking and login

Personalized Financial Technology Built to Scale

Tech.us helped bring Wealth Mastery to life as a digital platform that puts personalized financial planning tools directly in users' hands while supporting a large and growing audience.

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Tony Robbins Wealth Mastery app shown on tablet and phone

Why Tech.us

Built for Agents That Work Reliably in Production

We focus on the parts that determine whether an AI agent can act safely, recover from failure, and deliver consistent results inside real business systems.

We Recommend Against an Agent When Simpler Works

Deterministic automation is cheaper to run, easier to test, and more predictable. We propose an agent where the judgment between steps is the actual difficulty.

We Engineer the Tools as the Product

Tool granularity, validation, idempotency, and error semantics receive more attention than prompt wording, because that is where reliability comes from.

We Design for the Failed Run

Partial completion, compensating actions, and stateful escalation are specified before the successful path is built.

We Scope Permissions to the Requester

Agents act under the authority of the person who asked, which prevents autonomy from becoming a route around your existing access controls.

We Test the Path, Not the Reply

Evaluation covers complete runs under induced failure, and we hand over the evaluation set so your team can keep using it.

AI-Assisted Code Is Reviewed by an Engineer

Code written with AI assistance passes human review before it reaches a client system. Faster authorship does not move accountability.

why-choose-techus

HOW WE WORK

Start With the Workflow, Then Build the Agent

We map how the work happens today, define where an agent adds value, then build and test the system around real operating conditions.

1

Map the Process as It Runs Today

We document the actual steps, including the exceptions and manual workarounds that are rarely written down anywhere.  

2

Decide What Should Be an Agent

Predictable, rule-based portions are better served by deterministic automation. We recommend an agent only for the parts that genuinely require judgment between steps.  

3

Design the Action Surface

We define the tools, their scope, their validation rules, their permissions, and which of them require human confirmation.  

4

Build the Evaluation Set

We assemble real cases with known correct outcomes, including the awkward ones, before development starts.  

5

Build, Then Break It Deliberately

We develop the agent and test it against induced failures, ambiguous inputs, and unavailable systems rather than the intended path alone.  

6

Launch Narrow and Widen on Evidence

We release against a limited scope with human confirmation on consequential actions, then relax controls where measured reliability supports it. 

Production control

Let AI Agents Take Action Without Giving Up Control

Agents create value when they can act inside business systems. We define the boundaries and safeguards that make that access practical to operate.

Limit What the Agent Can Do

Give each agent only the access and actions required for the work it is responsible for.

Keep Critical Actions Behind Approval

Require human confirmation where an action carries significant business consequence or cannot easily be reversed.

Recover From Failed Runs

Plan what happens when a workflow stops partway through so incomplete work does not leave systems in an unknown state.

Keep Actions Traceable

Record what the agent attempted and what happened so completed workflows can be reviewed when needed.

Test the Complete Workflow

Evaluate whether the work was completed correctly rather than judging the agent only by the quality of its responses.

AGENT TECHNOLOGY

The Right Technology for the Workflow

We work across agent frameworks, integrations, workflow tools, identity controls, and monitoring technologies.
Technology choices are based on your systems, expected usage, security needs, and long-term maintainability.

Agentic AI & Orchestration

Agentic AI Agentic AI
AI Agents AI Agents
Multi-Agent Systems Multi-Agent Systems
Agentic Workflow Automation Agentic Workflow Automation
Model Context Protocol (MCP) Model Context Protocol (MCP)
Agent-to-Agent Protocol (A2A) Agent-to-Agent Protocol (A2A)
Agent Memory and Reasoning Agent Memory and Reasoning

FAQ

Questions Buyers Ask About AI Agents

The tool layer more than the model. Well-scoped actions, clear tool descriptions, validation before execution, repeat-safe operations, and useful error messages account for most of the difference between an agent that works and one that stalls. 

If the sequence is known in advance, deterministic automation is better in every respect. An agent earns its place when the next step depends on what previous steps returned, and that judgment is what currently occupies a person. 

That case is designed before the build. Steps that can be reversed have a defined compensating action, steps that cannot sit behind approval, and a stopped run escalates with a record of what was completed and what was not. 

Actions are separated from retrieval, each tool carries its own permission requirement, irreversible operations require confirmation, and arguments are validated against business rules before reaching your systems.

Not if it is built correctly. Access is evaluated against the person who initiated the task rather than against the agent's own credentials, which is the difference between an assistant and a route around your access controls. 

It happens when an AI agent has access to more information or actions than the person using it. A user could unintentionally or deliberately ask the agent to do something they are not allowed to do. We prevent this by making the agent follow the same permissions as the person who started the request. 

Over complete runs rather than single responses. We check whether the task finished correctly, whether the right tools were called with the right arguments, and how the agent behaves when systems are deliberately made to fail. 

By capping steps and elapsed time, bounding tool call volume, keeping context from growing across a run, and measuring cost per completed workflow rather than per model call. 

Usually not at first. A single agent with a clear responsibility is easier to evaluate and debug. Multiple agents are warranted when the process genuinely splits into distinct roles with defined handoffs.

Sometimes, through automation of the interface, though it is far less durable than an integration. We would generally recommend that route only where no interface exists and the process justifies the ongoing maintenance. 

Per project, driven by how many systems the agent must operate, whether those interfaces exist, the number of actions and approval paths required, and whether we run the system after launch. 

Ready to Put AI Agents to Work?

Bring us a workflow that is slowing your team down. We’ll identify where an AI agent can take action, where automation fits better, and what it takes to deploy it reliably.

Schedule an Agent Design Review

Process review · Action surface · Permission model · Practical next step