Home / Services / AI Agent Development

AI Agent Development Services

We build AI agents that do work inside your systems, for product and operations teams. An agent reads a ticket, a claim, or a candidate file, calls the tools your team already uses, and hands the result to a person where the action has consequences. Our AI agent development services cover the scoping, the agent, its guardrails, and the evaluation that shows whether it finishes the task on your data.

Discuss Your Project

What You Get
Solutions
Capabilities
Case Studies
Engagement Models
Why Choose Us
FAQ

AI Agent Development Services, From Scoping to Production

An AI agent is software that takes a goal, works out the steps, calls tools and systems to carry them out, and runs the checks defined for the task. A chatbot answers; an agent acts. An agent that acts without a defined boundary is the kind of project Gartner’s forecast describes. In June 2025 it predicted over 40% of agentic AI projects canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls. Our AI agent development services are scoped around those three causes. The value of finishing the task is ranked before the build, the cost per task is reported from the pilot, and the risk controls are written into the task boundary.

Our custom AI agent development starts with that task boundary: which actions the agent takes on its own, which it proposes for approval, and which it never takes. The pattern underneath, retrieval over verified sources with a person at the consequential step, already runs in production. Hello, We Hire ranks candidates for recruiters with automated pre-screening, and Environmental.AI drafts environmental assessment reports from a verified document base.

The pilot is a working agent on one task over your data and systems, planned at four to eight weeks. Scoping, two to three weeks before it, produces:

The task boundary for the first agent.
The tools and systems the agent will call, with the access each one needs.
An evaluation set of real tasks and the pass bar agreed with the people who do the work today.
A build estimate for the pilot.

Send us the task, and we will quote scoping as a fixed fee.

Agents sit on top of retrieval and integration work that has its own pages. See RAG development services for the knowledge the agent reads, AI integration services for connecting models to the product you already run, and the generative AI development hub.

Custom AI Agents We Build

Six kinds of agents cover most of the requests we scope. Each is built inside a task boundary and logs tool calls with their inputs and outputs. Three of the six extend patterns already in production for the clients in Case Studies.

Coding and Engineering Agents

Agents that take a task from the tracker to a reviewed pull request in your repository and CI. The agent plans, a person reviews the plan, the agent implements and runs the tests, and an engineer reviews the result. The agent drafts, engineers review.

Document Processing Agents

Agents that read claims, contracts, or invoices, extract the fields your process needs, check them against your rules, and write the result into the system of record. Exceptions go to a person with the reason attached, and the queue is left with the hard cases.

Customer Operations Agents

Agents that resolve a request: look up the account, apply the policy, and make the change, such as a refund or a reschedule, within limits you set. Above the limit, the agent drafts the action and a person confirms it. Routine requests close without a ticket.

Knowledge Agents in Slack and Teams

Agents that answer from your Confluence, Google Docs, PDFs, and tickets with the source on each answer, then draft the follow-up for a person to send. Archie, our hackathon Slack bot, already answers this way. The agent adds the draft for one person to review.

Screening and Review Agents

Agents that screen candidates or transactions against your criteria and rank them. Criteria are checked for bias against protected groups before go-live, and the evidence behind each ranking is logged for a hiring-tool audit. Reviewers start from the shortlist.

Report and Compliance Drafting Agents

Agents that pull from a verified document base and your project data, draft the report, and show the sources used, for your specialist to finish and submit. Environmental.AI runs this pattern, and report preparation time fell by up to 90%.

What Our Agentic AI Development Services Include

How We Scope the First Agent

Scoping ranks your candidate tasks by the value of finishing them and by how early a wrong action can be caught, then picks the task that goes to pilot first. For that task it writes the boundary in three tiers: actions the agent takes alone, actions it proposes for approval, and actions it never takes. The evaluation set and the pass bar are agreed before the build starts. A pilot team is an ML engineer, a backend engineer, and a project manager. The people who do the task today review the agent's output, and scoping ends with a build estimate you can decline.

Tool Access and Credentials

The agent reaches your systems through one schema per action that the model fills in and code validates before the call (function calling). Each tool carries its own credentials and the narrowest scope that action needs, so an agent that can read a ticket cannot close an account. We connect ticketing, CRM, ERP, repositories and CI, databases, and internal APIs. Where your vendors support the Model Context Protocol (MCP), a standard interface to a vendor's tools, we use it with the server's tool list pinned and reviewed first. Tool results and retrieved documents are handled as untrusted data, since a document or a web page the agent reads can carry injected instructions. Instructions found in that text cannot move an action between tiers, and injection attempts are part of the evaluation set.

Retrieval on Verified Sources

Agents that need facts get them through retrieval-augmented generation (RAG) over approved documents. The retrieved sources are attached to the answer, and a retrieval log shows what the model saw, so an unsupported statement can be traced to its step. Archie, our Slack RAG bot, was stress-tested on 600 documents from a client project. The community edition of our RAG platform, open source under Apache 2.0, is the starting point when you want to own the retrieval layer.

Guardrails and Human Approval

The three-tier boundary is enforced in the tool layer, and the model cannot promote an action from one tier to another. Each tier is a separate set of tools with its own credentials and parameter checks. Coding agents work on an isolated branch where merge is the human step. Approvals arrive where the approver already works, in the ticket, Slack, or email, with the agent's reasoning and the evidence attached. If no one answers by the deadline, the action is dropped. Before an action that matters, the agent must return a yes or no, a choice, or a score in a fixed format. Code validates that value before the action runs. Writing to your system is a single tool call that can be reversed or retried. A run that stops mid-task leaves its trace and the partial result in the approval queue. Caps on spend, volume, and rate stop a loop at the level you set. A single switch stops new tool calls. A step in progress completes or rolls back under that tool's own rules, and the audit log shows where the agent stopped.

Evaluation and Observability

The evaluation set is built from real tasks with known outcomes. The pass bar covers four numbers: tasks completed, disallowed actions attempted and blocked by the tool layer (the target is zero), cost per task, and time per task. Each run leaves a trace of the steps, tool calls, and model outputs, so a wrong result can be followed to the step that caused it. The set is re-run on any change of model, prompt, or tool. Drift monitoring in production, which checks that live inputs still resemble the evaluation set, covers the tasks the set does not.

Deployment, Model Choice, and Operations

The model sits behind an interface, and the endpoint can change without touching the agent. We deploy in your cloud account or ours, with open-weight models (ones you host yourself, such as Llama) when the data cannot leave. Routing sends routine steps to a cheaper model and reserves the larger one for the steps that need it, with the cost per task tracked from the first pilot run. The agent code, tool definitions, prompts, and evaluation set written for you are yours, and our open-source components stay under their Apache 2.0 license. After the pilot, we run the agent in production or hand it to your team.

Case Studies

Hello, We Hire

An AI recruitment platform with automated pre-screening.

Pre-screening handles 1,000 candidates per hour and hands recruiters a ranked list. It analyzed 30,000 CVs in the first month and saves about 20 hours per vacancy.

Read the entire case

Environmental.AI

Environmental compliance automation.

We built a RAG-based system that automates environmental assessment documents and regulatory templates, cutting preparation time by up to 90%.

Read the entire case

Revvel

AI-powered women’s health platform.

We developed an AI platform that turns elite health experts into digital twins and reviews years of patient data in under 60 seconds, delivering risk assessments up to 10x faster and cutting time-to-care from days to minutes.

Read the entire case

InnerPeak.AI

Student mental wellness platform.

We developed a comprehensive web and mobile application that provides students and teachers with 24/7 personalized support and engaging resilience training to build essential life skills.

Read the entire case

View all case studies

Flexible Developer Hiring Model

Your priorities define the way we work together — from how much control you want to the level of flexibility your project demands.

Team Augmentation

Quickly scale your workforce with skilled developers who integrate seamlessly into your in-house team, filling talent gaps and boosting productivity.

Dedicated Development Team

A whole, cross-functional team working exclusively on your project, providing end-to-end development, management, and long-term support.

Software Development Outsourcing

Delegate your entire project to our specialists and focus on business growth, while we handle development, delivery, and quality.

Why Plus8Soft?

Experience
Multiplied by AI

We blend deep engineering expertise with cutting-edge AI acceleration. By integrating intelligent tools into our workflow, we don't just write code—we engineer solutions faster and with higher precision.

Business-First
Transparency

We look beyond the ticket. Our team operates with hyper-transparency, treating your budget and goals as our own. We align technical decisions with your business strategy to create real, measurable value.

Committed to
Overdelivery

Meeting requirements is our baseline; exceeding them is our culture. Whether it's optimizing performance, refining UX, or anticipating future scalability, we consistently go the extra mile.

AI Agent Development FAQ

What is the difference between an AI agent and a chatbot, and which one do we need?

A chatbot takes a message and returns an answer. An agent takes a goal, works out the steps, calls tools and systems to carry them out, and runs the checks defined for the task. If the work ends when the user has the answer (support, FAQ, lead capture), you need a chatbot, and we build those separately. If the work ends when something has changed in a system (a ticket closed or a claim routed for payment), you need an agent. The design question is then which of those changes a person must approve.

How long does it take to build an AI agent with Plus8Soft?

Scoping takes two to three weeks. The pilot, a working agent on one task over your data and systems with the approval tier in place, is planned at four to eight weeks after that. The figures are our planning estimates and are confirmed in the scope. The pilot ends with a go/no-go on the four numbers it reports. A task that misses the bar goes back to scoping. Production rollout depends on how many systems the agent touches and on the review your organization requires. A first rollout usually falls within the two to four months our generative AI development hub quotes for a focused AI feature.

How much does custom AI agent development cost?

Scoping is a fixed fee quoted before it starts. The pilot is priced per task from the scope, and each further agent or task is estimated on its own. The cost follows the number of tools and the depth of the approval flow more than the model. Model usage fees are paid to the provider and scale with the number of tasks. The cost per task is one of the four numbers the pilot reports.

How do you stop an agent from taking a wrong action?

The tool layer decides what the agent can do. Each tier of actions has its own tools, credentials, and parameter checks, so the model cannot talk its way from reading a record into changing one. Consequential actions wait for a named approver, who sees the reasoning and the evidence where they already work. An unanswered request is dropped at its deadline. Spend, volume, and rate caps contain a loop, and a single switch stops new tool calls while the audit log shows what ran.

Which models and frameworks do you use for agents, and can we bring our own?

We build on OpenAI, Anthropic, and Google models and on open-weight models such as Llama. We use LangChain, LlamaIndex, or Haystack when the task needs a framework, and plain code otherwise. The model sits behind an interface, so if your security team has already approved a provider, we build on it.

Can the agent run in our cloud without data leaving it?

Yes. The agent and its tools deploy in your cloud account, and an open-weight model hosted there keeps the data inside it. When a hosted model is the better choice, data goes only to endpoints whose enterprise terms exclude training on your inputs, with zero-retention endpoints where the provider offers them. Credentials for your systems stay in your secrets store.

How do you measure whether the agent works?

Against an evaluation set of real tasks with known outcomes, agreed with the people who do the work today. The pilot reports the four numbers from that set, and each run keeps a trace. Your team can open any result and see the steps behind it. The same set runs after any change to the model, prompt, or tools, and a regression on the set then shows up before production. Drift monitoring covers the tasks the set does not.

Can you add agents to a product or workflow we already have?

Yes. The tool layer is built on the APIs your product already exposes, and the agent ships without a rewrite. The first task is usually one your team does by hand today, with the approval step placed where that person signs off now. Later agents reuse the same tools, guardrails, and evaluation pipeline.

When would you tell us to use a vendor's agent platform instead of a custom build?

On the first call or in scoping, when the task lives inside one vendor system that already exposes the tools and the approval step, we say so. We build custom when the agent crosses systems, when the boundary has to be enforced in code you control, or when the data cannot leave your cloud. The scope you receive says which case yours is.

Who runs the agent after the pilot?

Your team or ours. We can run it in production, with monitoring, evaluation re-runs, and model updates, or hand it over with the traces, the runbook, and the evaluation pipeline. The agent code, tool definitions, prompts, and evaluation set written for you are yours in both cases. Our open-source components stay under their Apache 2.0 license, which lets you use and modify them.