AI engineering
AI agents that own a job end to end, not a chat window.
An AI agent is software that carries out a multi-step job on your behalf: it reads the context, decides what to do next, acts in your systems, and reports what happened. AISphereX builds custom AI agents for specific workflows such as lead follow-up, document chasing, data entry, reconciliation and reporting. We map the workflow first, connect the agent to your CRM, inbox, storage and databases, put limits and approvals around what it can do, and run it in production.
The useful question is not which model to use. It is which job you want done without a person doing it, and whether that job is defined tightly enough to hand over. Most agent projects fail on the second half, so that is where we start.
What you get
The scope, in plain terms.
- One job, defined and scoped
- We write down the trigger, the steps, the decision points, what done looks like and what the agent must never do. That document is the actual deliverable of week one, and it is what makes the rest buildable.
- Integrations into your systems of record
- CRM, email, calendar, storage, spreadsheets, databases, helpdesk, billing. The agent works inside the tools your team already uses so nobody has to log into a new platform.
- Guardrails and approvals
- Hard limits on what the agent can touch, human approval on anything with money or a customer at the end of it, and a full record of every action taken.
- Handling of the awkward cases
- What happens on a missing field, a duplicate record, an ambiguous instruction or an API that is down. Agents fail on these, not on the main path.
- Observability
- Logs you can read, a view of what the agent did and why, and alerting when it starts behaving differently.
- An owner after launch
- We stay on it. Models change, your process changes, edge cases surface. An agent nobody maintains degrades quietly.
How it runs
From first call to something live.
- 01
Pick the job
We look at what your team repeats, how often, and how much of it is judgment versus procedure. Then we pick one job worth automating, not five.
- 02
Write the spec
Trigger, steps, data, decisions, limits, escalation, definition of done. You approve it before we build.
- 03
Build and connect
The agent, the integrations and the guardrails, built against your real data in a safe environment.
- 04
Shadow run
The agent runs alongside your team and proposes actions without taking them, so you can see where it is right before it has permission to act.
- 05
Hand over the keys, gradually
It starts acting on the safe subset, then widens as it earns it. We keep the approval step wherever the cost of a mistake is real.
What it is built on
Chosen per project, not per fashion.
- Reasoning
- A current frontier model, chosen for the job. Cost, latency and reliability differ enough between them that the choice is a design decision, not a default.
- Your knowledge
- Retrieval over your real documents, policies and records so the agent answers from what is true in your business rather than what is plausible.
- Actions
- Typed, permissioned tools that call your systems, so the agent can only do things you have explicitly allowed.
- Orchestration and queues
- Retries, rate limits, scheduling and failure handling, the same way any production background job is built.
Is this you
We would rather disqualify early than waste your quarter.
This is a good fit when
- A repetitive multi-step job that has to happen and follows knowable rules.
- Work that falls through the cracks when your team is busy, such as follow-ups and chasing.
- A process that spans three or four systems and currently spans them via a person copying between them.
- You can point at examples of the job being done well.
It is the wrong call when
- The job is genuinely judgment-heavy and changes every time. An agent will make it worse.
- The process is undocumented and contested internally. Automating a disagreement just makes it faster.
- You want an agent because it is an agent. If a scheduled script solves it, we will tell you and build the script.
Proof
We did not learn this on client projects.
ViveLead is our own CRM and HRMS platform. We designed it, built it, shipped it on web, Android and iOS, and we run it in production for paying customers. Everything we do for clients is done by the team that has to answer for that.
See the case study →Questions we get asked before signing.
A chatbot answers questions. An agent does work. A chatbot tells your customer what your refund policy is; an agent checks the order, applies the policy, issues the refund inside your billing system and logs it. The engineering difference is that an agent needs permissions, guardrails, error handling and an audit trail, because it changes things.
No. Most of our clients have data spread across a CRM, a shared drive and a few spreadsheets, and that is the normal starting point. Part of the build is getting the agent reliable access to it. If something genuinely blocks the job, we surface it during scoping rather than discovering it halfway through.
Three ways. It can only call the specific actions we have given it, so the blast radius is bounded by design. Anything involving money or a customer-facing message can require human approval. And every action is logged, so when something does go wrong you can see exactly what happened rather than guessing.
Whichever fits the job, and we will tell you which and why. We build so the model can be swapped, because the current best choice for a given task has changed several times a year and will keep doing so. Being locked to one vendor is a design flaw, not a feature.
Usually it takes the part of their week that nobody wanted. Be direct with us about the intent either way, because it changes what we build and how we roll it out. Agents deployed against a team that was not told tend to get quietly worked around.
It depends almost entirely on how many systems the agent has to touch and how well defined the job is, not on the AI part. A single well-scoped job against two systems is a different order of magnitude from an agent spanning a legacy ERP. We scope before we quote, and the scoping output is yours whether or not you build with us.
Related
Most clients end up combining these.
Next step
Thinking about ai agents?
You get a scoping conversation with the people who would do the work, not a sales call. If we are not the right fit we will say so on that call.