Telecom conversational AI agent
70% deflection, under 3 seconds average response, handling bill payments, plan upgrades and SIM management through live calls into billing, CRM and provisioning. Web, WhatsApp and IVR.
AI Agent Development Services
Agents that complete work inside your systems, with scoped authority, rollback and an audit trail. In production today handling live customer transactions.
An AI agent is software that completes a multi-step task on your behalf: it reads context, decides what to do, calls your systems and finishes the job. AMT builds custom AI agents for enterprise operations and customer-facing use, with scoped permissions and human review gates on anything irreversible. We have been building AI since 2013 and are ISO 27001:2013 certified.
// the_distinction_that_matters
If it can answer but cannot act, it is not an agent
The difference is not the model. It is tool access, state, error handling and the authority to write to a real system. A chatbot tells a customer how to upgrade their plan. An agent upgrades it, confirms it, and rolls it back cleanly when the payment fails halfway through.
That is also why agents fail in production. Retrieval drifts, an API changes shape, a step half-completes and nothing undoes it. The engineering around the agent is the whole job.
// the_candidate_test
Is your process a good candidate for an agent?
Agents suit some work and waste money on the rest. A process is a good candidate when all four of these are true.
- It happens often enough that automation pays back. A weekly task rarely does. Count the actual volume before anyone builds anything.
- The steps are describable. They do not have to be written down today, but somebody has to be able to describe them without saying "it depends" more than twice.
- The systems involved have APIs, or can be given them. An agent that cannot reach the system cannot complete the task, and screen-scraping a system you do not control is a support burden, not a solution.
- Someone can define what a correct outcome looks like. If nobody can score it, nobody can test it, and an untestable agent cannot safely be changed after launch.
If the process is rare, undocumented in a way nobody can reconstruct, locked behind systems with no programmable interface, or impossible to score, an agent is the wrong tool and we will say so.
Then the harder question: what may it do alone?
Three answers decide most of the cost and all of the risk.
- What may it read? Which systems, which records, filtered by whose permissions.
- What may it write? This is the line between a useful agent and an expensive demo, and it is also the line where your security review begins.
- What must it hand to a human? Anything irreversible, anything above a value threshold, anything a regulator would ask about.
Check your process against this properly
The four conditions plus the authority questions we ask in discovery, on one page. Run it against two or three of your processes and the right first candidate usually becomes obvious.
// what_we_build
What we build
- Task agents
- Multi-step workflows executed end to end, with rollback.
- Customer-facing agents
- Transactional agents on web, WhatsApp and voice.
- Internal operations agents
- Document processing, triage, routing, reporting.
- Multi-agent systems
- Orchestrator, specialist and reviewer agents with defined scopes.
- Agent evaluation
- Harnesses measuring accuracy, cost and latency per release.
// method
How we build agents that survive production
- Scope the authority. What may the agent read, write and decide alone.
- Ground the retrieval in your real documents, not a sample set. Detail on the RAG page.
- Wire the tools. Real API integration with authentication, retries and idempotency.
- Build the evaluation harness before the agent goes near a customer.
- Human review gates on anything irreversible.
- Observability. Every decision logged and auditable.
// proof
Proof
AI insights agent in a SaaS product
Analyses live conversation logs and recommends specific flow changes with reasoning attached. 35% higher flow completion.
Across deployments
Three AI agent deployments to date, average cost reduction of 40%.
// cost
What an agent costs
The cost of an AI agent depends less on the model itself and more on the systems it connects to, the actions it performs, and the reliability and governance required in production.
| Type | Typical complexity | What drives the effort |
|---|---|---|
| Read-only AI assistant | Low | Primarily knowledge ingestion, retrieval/RAG, prompting, guardrails and evaluation. Little or no enterprise system integration. |
| Production AI agent | Medium | Typically integrates with 2-3 enterprise systems. Effort is driven by APIs, authentication, business rules, error handling, security and production monitoring. |
| Multi-agent system | High | Multiple specialised agents coordinate tasks and actions. Complexity comes from orchestration, state management, tool access, observability, evaluation and failure handling. |
The model is close to the smallest line in the budget. An agent that reads is a fraction of an agent that writes to billing, and the difference is integration work rather than AI work.
// the_honest_section
Who this is not for
If the process runs a few times a month, automation will not pay back. Fix the process first and revisit.
If the systems have no APIs and no route to one, the integration problem comes before the agent problem.
If nobody can say what a correct outcome looks like, we cannot test it, which means we cannot safely change it after launch. That is a bad place to be with software that talks to your customers.
// faqs
Frequently asked questions
What is an AI agent?
Software that completes a multi-step task on your behalf. It reads context, decides what to do, calls your systems and finishes the job, within permissions you define.
What is the difference between an AI agent and a chatbot?
A chatbot answers. An agent acts. The engineering difference is tool access, state management, error handling and the authority to write to real systems.
What processes are a good fit for an AI agent?
High frequency, describable steps, systems with APIs, and a definable correct outcome. All four. The candidate test above walks through it.
How do you stop an agent doing something wrong?
Scoped authority, human review gates on anything irreversible, idempotent operations so a retry cannot double-charge, rollback paths, and full audit logging. The controls are designed before the agent is built, not after.
Can an AI agent write to our production systems?
Yes, and that is where the value is. It is also where the security review lives, so write access is scoped narrowly, logged completely, and gated on anything irreversible.
How long does it take to build an AI agent?
A first production agent with two or three integrations typically takes three to six months including discovery. Integration effort sets the timeline more than anything else.
What does AI agent development cost?
See the table above. Integration count is the main driver, not model choice.
Do we need RAG as well?
Usually yes, if the agent needs to know things that live in your documents. Retrieval and action are separate problems and both have to work. See RAG development.
Tell us which process you had in mind
Thirty minutes with an engineer who has shipped agents into production. We will tell you whether it is an agent problem, an automation problem, or neither.
Not ready to talk? Read how we deliver, or see what software development costs. No form.