AI agent development
Easital Technologies Ltd. designs, builds and runs custom AI agents: software that uses a language model to plan the steps of a task, call the tools it needs and check its own work. We take an agent from a scoped use case to a monitored production system, and we operate agent-based products of our own.
An AI agent is software that uses a language model to work toward a goal: it decides the next step, calls a tool to carry it out, reads the result and repeats until the task is done or it hands over to a person.
That loop separates an agent from a chatbot, which answers a message and stops, and from a scripted automation, which follows steps a developer fixed in advance. An agent chooses its steps at run time. That lets it handle requests nobody scripted, and it also means it can choose badly. Most of the engineering in an agent project goes into limiting and checking those choices.
An agent is the right tool when a task needs judgment across several steps and the inputs vary too much to script: answering a phone call, triaging a support ticket or changing code in a repository. When the steps are always the same, a plain workflow is cheaper and more predictable, and we say so during scoping.
| Scripted automation | LLM chatbot | AI agent | |
|---|---|---|---|
| Who decides the next step | The developer, in advance | The user, one message at a time | The model, at run time, within set limits |
| Acts in other systems | Yes, on fixed paths | Usually not | Yes, through the tools it is allowed to call |
| Fits best | Stable, repeatable processes | Questions and answers | Multi-step tasks with varied inputs |
| Main risk | Breaks when inputs change | Wrong or invented answers | Wrong actions, loops and runaway cost |
Our custom AI agent development services cover six kinds of agentic system. Each is built around your data, your tools and your rules.
Multi-step work that moves across your systems: read an inbound request, look up the account, draft the reply or the record change, and stop for human approval where the stakes call for it.
The layer that lets a model act: typed tool definitions over your APIs, databases and internal services, with permissions, input validation, retries and a log of every call.
Several specialized agents coordinated by a planner or a fixed graph, for work that splits cleanly into roles such as research, drafting and review. We use it when one agent cannot hold the job, not by default.
Agents that hold a phone conversation: speech-to-text, a language model that decides what to say and do, text-to-speech, and the telephony that connects them to real numbers, call transfers and SMS.
Agents that read a codebase, edit files and run the result in an isolated sandbox with a live preview. This is the core of Manob.ai, our own vibe-coding platform.
Agents that watch infrastructure and act on what they find. Easital has built an autonomous server self-healing system on this pattern.
A build runs in seven steps, from a written scope to an agent that is monitored in production. The evaluation set comes before the agent, because it is the only way to tell whether a change helped.
We pick one task with a clear owner and write down what done looks like, which systems the agent may touch and what a mistake would cost.
Before any agent code, we collect real examples of the task with the outcome a competent person would produce. Every later change has to pass this set.
One model, a short instruction and only the tools the task needs. We run it against the evaluation set and read the failures, which say more than the pass rate does.
Permissions per tool, validation of inputs and outputs, limits on steps and spend per run, and human approval for actions that cannot be undone.
We measure tokens and time per run, then reduce both: smaller models for simple steps, cached context, shorter prompts and fewer round trips.
The agent goes live in stages. Every run is traced step by step, failed and unusual runs are flagged for review, and alerts fire on error rate, latency and spend.
Production failures go back into the evaluation set, so the same mistake is caught before the next release. The agent, its evaluation set and its runbook are documented so that your team can take over.
Three live systems show these patterns in production: one built for a client and two that Easital owns and runs.
Client project
Built by Easital for a client in the United States
Voice agents that answer real phone calls, book appointments and qualify leads. The Calldone site describes agents with tools for transfers, SMS, knowledge-base queries and webhooks, connected to carrier numbers or to a customer’s own SIP or PBX.
Easital product
Built and run by Easital
An agentic code editor inside a cloud sandbox, with live preview and one-click deployment. A multi-step agent task uses more of the model than a simple prompt, which is the cost-control problem every agent product has to solve.
Easital product
Built and run by Easital
An AI pipeline with no chat window. A screen recording goes in, and a narrated how-to video, a written guide and a share page come out. Language and speech models from more than one provider run in a single pipeline.
Multi-agent LLM systems fail mostly because of how they are designed and coordinated, not because the model is weak: the task is specified loosely, agents lose or distort information when they hand work to each other, and nothing verifies the result before the system stops.
That is the finding of Why Do Multi-Agent LLM Systems Fail?, a 2025 study led by researchers at UC Berkeley. The authors annotated more than 1,600 execution traces from seven multi-agent frameworks and identified 14 failure modes in three categories: system design issues, inter-agent misalignment and task verification.
The practical consequence is that every added agent adds places to fail. We start with one agent and move to several only when the evaluation set shows that a single agent cannot do the job. When we do build a multi-agent system, each failure category gets its own countermeasure.
| Failure category | What it looks like | What we do about it |
|---|---|---|
| System design issues | An agent ignores its role or the task rules, repeats steps, or does not know when to stop. | Narrow roles written as testable instructions, fixed limits on steps and explicit stop conditions. |
| Inter-agent misalignment | An agent withholds or loses context, ignores another agent’s output, or the exchange drifts off the task. | Handoffs with a defined structure in place of free text, and one shared record of task state. |
| Task verification | The system ends early or reports success without checking, or the check is too shallow to catch the error. | A separate verification step with its own criteria, tested against the evaluation set, and human review for costly actions. |
Models and infrastructure are chosen per project, by running them against the evaluation set. We are not tied to a vendor, so the parts are listed by what they do.
We work with all major commercial model providers and with open-weight models. The model is chosen per step for quality, speed and cost, and the agent is built so that it can be changed later.
Commercial and open speech engines, selected for language coverage, response time and price.
The main cloud telephony carriers, and existing phone systems over SIP.
There are three ways to engage Easital for agent work, depending on whether you need a finished system, more engineers or a second opinion.
We take one agent from scope to production through the seven steps above, then hand it over or keep operating it.
Best for: a defined use case with a clear owner on your side.
You can hire agentic AI developers from Easital who work in your repository, backlog and review process. Details are on the hire AI developers page.
Best for: teams that already have an agent roadmap and need more hands.
We review an agent that is unreliable, slow or expensive: its traces, prompts, tools and evaluation coverage, followed by a written list of fixes in priority order. See also LLM cost optimization.
Best for: agents that work in a demo and fail with real users.
An AI agent development company, also called an agentic AI development company, designs, builds and operates software in which a language model plans steps and calls tools to complete a task. The work covers scoping the task, building the tools and guardrails around the model, testing the agent against real examples and monitoring it in production.
A chatbot replies to a message. An AI agent takes actions: it decides which step comes next, calls tools such as an API, a database or a phone line, and continues until the task is finished. For conversational interfaces on their own, see AI chatbot development.
Mostly for design reasons. A 2025 study led by UC Berkeley researchers grouped the failures it found into system design issues, misalignment between agents and missing or weak verification of the result. We start with a single agent and add more only when testing shows the need.
By limiting what it can do and checking what it does. Each tool has its own permissions, inputs and outputs are validated, runs are capped in steps and spend, and actions that cannot be undone wait for human approval. Every release is tested against an evaluation set built from real cases.
Running cost comes from the number of model calls per task, the amount of context sent with each call, the model used for each step, and any speech or telephony minutes. We measure cost per run from the first prototype and reduce it with model routing, caching and shorter context. Easital has built LLM token-cost optimization before, and it is also offered on its own as LLM cost optimization.
Yes. An agent can call hosted model APIs, open-weight models on your own servers, or a mix of both. Easital has done model training and has set up local and on-premise models, which matters when data cannot leave your network. See private LLM deployment.
Describe the task, the systems involved and what a mistake would cost. We reply by email with questions and a proposed first step.
Easital is an AI and SaaS engineering company that takes AI software to production, and runs AI products of its own. Founded in 2019.