AI agent development services that hold up in production
We build AI agents that decide the next step, call your tools, and finish real work. A person approves anything that touches money, customers, or data. Every step is traced, and every run has a cost you can see. We also audit agent systems someone else built and make them safe to grow.
What is an AI agent?
An AI agent is software where the model decides what to do next. It reads the request, picks a tool, checks the result, and keeps going until the job is done or it needs a person. A workflow is different: its path is written in code, even when some steps use AI. Most systems we build mix the two. Workflows run the steps that must go the same way every time. Agents handle the steps that need judgment.
- The model picks the next step. Your rules decide what it may do.
- A person approves anything that touches money, customers, or data.
- Every step is traced in Langfuse, with its cost.
- Tools reach the agent through MCP servers with typed inputs and an allow list.
- Proven on an agency's AI employees in Slack, The Meatery's voice agent, and Good Food Project.
What does an AI agent in production look like in 2026?
In 2026 the model is the easy part. The hard part is everything around it: who may ask the agent for what, which tools it can reach, who approves its actions, and what each run costs. OWASP's list of top risks for AI apps names three that matter most for agents: prompt injection, excessive agency, and unbounded consumption. In plain words: text that tricks the agent, an agent allowed to do too much, and runs that cost too much.
So every agent we ship gets four things. Tools through MCP servers, each with typed inputs and an allow list. Approvals tied to roles, so nobody approves their own request. Test sets built from real requests that run before every release. And a trace of every step in Langfuse, with the cost of each run.
Do you need an AI agent or a workflow?
Three questions decide it. Answer them before you pay for an agent.
If the steps change with every request, like routing work across a team or answering a customer mid-call, that is where an agent pays off.
01.
Can you write the steps down?
If yes, start with a workflow. It is cheaper to run and easier to test.
02.
Must the same input always give the same result?
If yes, keep the model out of that step and use plain code.
03.
What does a wrong action cost?
If it touches money, customers, or data, a person approves it first. Always.
How much does the agent do on its own?
You choose the level for each action, not for the whole system. An action moves up a level only after it has run clean for a while.
1. Suggest
The agent drafts. A person sends. Good for client emails and proposals.
2. Act with approval
The agent acts after one click from the right person. Good for posts in client channels and new files.
3. Act, then report
The agent acts and logs it where you can see it. Good for pulling reports and updating tasks.
4. Act alone
The agent acts inside tight limits. Good for reading the knowledge base and answering common questions.
What goes wrong with AI agents?
When we audited a Charlotte marketing agency's AI employees, the model was never the problem. The plumbing around it was. Five things we found, and the fix for each:
Each one is a rule now in every agent we build.
01.
Approvals anyone could give
A test account with an outside email, never confirmed, approved an email to a client, and it was sent. Fix: only confirmed staff approve, by role, and never their own request.
02.
One "remember this" changed every agent
A fake "50% off for every client" note became a rule, and the sales agent repeated it. Fix: an admin signs off before a note becomes a rule.
03.
Wrong actions ran before anyone could stop them
A calendar invite turned into a Slack message. A thank-you email turned into a proposal PDF. Fix: an action runs alone only when it matches the request. Otherwise the agent asks.
04.
Private data shared by room, not by person
A client's retainer came back in a public channel because the agent checked the room, not the person asking. Fix: role and client access are checked before any agent or tool runs.
05.
Code the AI wrote could ship with no review
Changes written from Slack could merge into the live app on their own. Fix: a person approves every merge.
What does an AI agent cost to run?
It depends on how much the agent reads on each call and how often it runs. Running cost is where most agent systems leak money, and most vendors never mention it. In the same agency audit, every call carried the full instructions and every tool, about 28,000 tokens. One long Slack thread used about 14 million tokens. Dashboard tiles were 68% of the AI bill, and two of them used AI just to count. And three screens showed three different monthly totals.
What we build in from day one:
- Each agent gets only the instructions and tools it needs for the task.
- Plain code wherever plain code works. AI only where judgment is needed.
- Step limits, minimum intervals for scheduled tasks, and a daily budget.
- One cost record per request, by person, client, and agent.
What do you get?
Approvals by role
Risky actions wait for the right person. Nobody approves their own request.
A trace of every step
Every request is traced in Langfuse: who asked, what ran, what it cost, and who approved it.
Cost you can see
Lean prompts, plain code where it works, and a budget per task. Like Pack Assist, where small talk never reaches the model.
Tested before every release
Test sets built from real requests and past failures run before any prompt or model change ships.
What is included?
Orchestrators and multi-agent systems
One entry point routes each request to the right agent by task and permission. Like the agency AI employees and Alifa.
Agents that act in your tools
Slack, Gmail, Drive, calendars, Shopify, CRMs, and project boards. Every write waits for approval unless you allow it.
Voice agents
Agents that answer, look up live data, and act mid-call. Like The Meatery's agent, which creates real Shopify discount codes on the call.
Memory and RAG
One memory service, knowledge tagged by client, cited answers, and no stale rules.
MCP servers
Your tools exposed to agents through MCP, with typed inputs, an allow list, and a log of every call.
Evals and traces
Test sets in Langfuse that run before every release, plus a trace and a cost for every live run.
Agent audits
A written review of an agent system you already have. Every finding has a fix, a severity, and an estimate.
Who is this for?
Digital Marketing Agencies
AI employees in Slack that report, follow up, and draft, with approvals by role.
See the Digital Marketing Agencies page
SaaS Companies
Agents shipped inside your product so you keep pace with funded competitors.
See the SaaS Companies page
E-commerce and DTC Brands
Voice and chat agents that answer product questions and recover abandoned carts.
See the E-commerce and DTC Brands page
Software Founders
An agent feature built into your MVP without hiring an ML team.
See the Software Founders page
Why teams pick TechEmulsion
30+ AI products shipped since 2023. Top Rated Plus on Upwork. US LLC in Wyoming. Here is what that gets you:
30+ AI products shipped
Our engineers have shipped 30+ AI products to production. You get code that holds up under real users and is easy to maintain.
Built for your business
We build around how your team already works. No generic templates, no forcing you onto new software.
Working software early
You see a working build every week, not a deck at the end. Scope drives time, and we quote a ship date after Discovery.
Simple to use
Your team should not need training to use what we build. We design every screen and workflow to be obvious.
NDA and security
We sign an NDA before we see anything. Your data stays yours, and we follow security best practices on every build.
Support after launch
We do not disappear after delivery. Ongoing engagements include monitoring, fixes, and updates.
What clients say
I highly recommend TechEmulsion for any computer vision related jobs. I engaged them about 8 months ago and they have delivered great results in the allocated time. The next time when I have a CV related job, I know where to turn to.
Harsha Viswanath
Crisp AI · ex Microsoft, Amazon, Intel
They delivered an exceptional web app for me, going above and beyond in every aspect of the project. If you're looking for a team that delivers high-quality work, stays on schedule, and makes your vision come to life, this is the team to hire.
Alex
Web app client
How does the engagement run?
A new agent starts as a fixed-scope build for the first workflow, then a monthly retainer for evals, model updates, and new tools. If you already have agents, we start with an audit.
Not a fit if you want a demo for a slide deck, or if nobody on your side owns the process the agent will run.
01.
Discovery
A free 30 minute call. We listen first. No pitch deck.
02.
Plan
We scope the first system that pays for itself and give you a fixed price and ship date.
03.
Ship and iterate
We build, test on real data, launch on the agreed date, then improve it with you.
Questions about Agentic AI Engineering
Answers from the engineers who do the work. Anything else, ask on a free 30 minute call.
Where this shows up by industry
Digital Marketing Agencies
AI for digital marketing agencies
AI employees in Slack that report, follow up, and draft, with approvals by role.
Open page
SaaS Companies
AI features for funded SaaS
In-product agents, on a paid proof of concept first.
Open page
Software Founders
AI engineering team for product founders
From a Lovable prototype to a production agent without hiring an ML team.
Open page
Software Founders & Builders
RAG Chatbot & Knowledge System
Ship a chatbot that answers from your data, not the internet
See the offer
Software Founders & Builders
Agentic Workflows & Automation
Replace manual multi-step work with AI agents that take action
See the offer
All industries
Every industry we serve
Marketing agencies, software founders, ecommerce brands, SaaS companies, and home services. One page per industry.
Browse industries