AI Agents · Custom AI Agent Development Services
AI Agent Development Company
An AI agent is software that uses a language model to plan steps, call tools and finish a task, not just answer a question. Aaga is an AI agent development company that builds autonomous and human-in-the-loop agents for real business workflows, on top of our own platform for workflows, permissions and integrations.
- Automate multi-step work across your CRM, ERP, inbox, documents and databases
- Keep people in control with approval steps, permissions and audit logs
- Measure every agent against test cases before it touches live work
- Start with one workflow as a pilot, then expand what the agent can do
- One workflow, measured before it scales
- Pilot first
- Workflows, permissions and integrations ready to reuse
- Own platform
- Reply to your inquiry within one business day
- 1 day
What Kinds of AI Agents Do We Build?
We match the level of autonomy to the risk of the task. Low-risk work can run end to end. High-stakes steps get a human approval.
Workflow Agents
Agents that run a defined business process, such as order intake, invoice matching or onboarding, and make judgment calls at each step.
Autonomous Task Agents
Agents that take a goal, plan the steps, use tools and report back, for research, data clean-up, monitoring and back-office tasks.
Multi-Agent Systems
Several specialized agents, such as a planner, researcher and reviewer, coordinated by an orchestrator for complex jobs.
Knowledge Agents
Agents that search your documents, wikis and databases with retrieval-augmented generation (RAG) and act on what they find.
Operations Agents
Agents for finance, HR, sales ops and IT that read email, update records, draft replies and raise tickets.
Customer-Facing Agents
Voice and chat agents that talk to customers and complete tasks in your systems during the conversation.
The Engineering Behind Reliable AI Agents
Agent projects more often fail on reliability than on the model. These are the parts we treat as core engineering, not afterthoughts.
Tool Use and Function Calling
Well-defined tools with typed inputs, clear descriptions and safe defaults, so the model calls the right action with the right data.
Model Context Protocol (MCP)
MCP servers that expose your systems and data to agents in a standard way, reusable across models and agent frameworks.
Retrieval and Memory
RAG over your knowledge, plus short- and long-term memory so agents keep context across steps and sessions.
Guardrails and Permissions
Role-based access, spending and action limits, input and output checks, and approval gates for sensitive steps.
Evaluation and Testing
Test suites of real tasks, automated scoring and regression checks every time a prompt, tool or model changes.
Observability
Traces of every step, tool call and decision, with cost and latency tracking, so you can see why an agent did what it did.
How an AI Agent Project Runs
Map the Workflow
We walk through the process with the people who do it today and mark where judgment, data and approvals are needed.
Design Agent and Tools
We define the agent's goal, tools, permissions and handoff points, and write the test cases it must pass.
Build the Pilot
We build on our own platform components for workflows, permissions and integrations, so the pilot reaches real data quickly.
Evaluate and Harden
We run the agent against the test suite, fix failure modes and add guardrails until results meet the agreed bar.
Launch With Oversight
The agent goes live with human approval on key steps. As it proves reliable, we widen what it can do on its own.
An AI-Native Team for AI Agent Development
Large IT services firms are built for big, multi-year programs, and are often the right choice for those. Agent projects usually move faster when they start small.
| Aaga | Typical large IT services model | |
|---|---|---|
| How AI fits in | AI agents are how we build by default | AI often sits in a separate practice or center of excellence |
| Who you work with | Senior engineers who design and build the agent | Layers of account, program and delivery management |
| Starting point | One workflow as a scoped pilot | Assessment phase and a larger program roadmap |
| Building blocks | Own platform for workflows, permissions and integrations | Often built per program or based on partner platforms |
| Cost | Affordable, scoped to the workflow | Priced for enterprise-scale programs |
Comparison describes typical delivery models, not any specific company.
What Is AI Agent Development?
AI agent development is the work of designing, building and running software agents that use large language models to complete tasks with tools. A useful agent needs more than a prompt. It needs well-designed tools, access to the right data, permissions, tests and monitoring. That is the engineering Aaga does.
Typical agent tasks include reading an incoming email, pulling the related order from your ERP, checking it against a policy, drafting a reply and updating the CRM. A person used to do every step. An agent can do most of them and ask for approval only where it matters.
Autonomous vs Semi-Autonomous Agents
Not every task should run without a human. We decide the level of autonomy per step, based on risk:
| Level | What the agent does | Good fit for |
|---|---|---|
| Assistant | Suggests the next action, a person executes | High-stakes decisions, new processes |
| Semi-autonomous | Executes most steps, pauses for approval on key actions | Payments, customer emails, record changes |
| Autonomous | Runs end to end, reports results and exceptions | Data clean-up, monitoring, research, internal reports |
Starting semi-autonomous lets you see the agent's decisions, build trust with real evidence and move steps to full autonomy one by one.
Single Agents, Multi-Agent Systems and MCP
Many problems are best solved by one capable agent with good tools. Larger jobs, such as preparing a due-diligence pack or processing a complex claim, can benefit from multi-agent systems: a planner splits the work, specialist agents handle research, extraction or drafting, and a reviewer agent checks the output before a person sees it.
We increasingly expose company systems through the Model Context Protocol (MCP). An MCP server wraps a system, for example your CRM or document store, as a standard set of tools and resources. Any compatible model or agent can then use it, which keeps integrations reusable as models change.
How Do We Keep AI Agents Safe and Reliable?
Reliability is designed, not hoped for. On every agent project we:
- Scope permissions tightly. Each agent gets only the data and actions it needs.
- Add guardrails. Input checks, output validation, action limits and protection against prompt injection from emails or documents.
- Keep humans in the loop for sensitive actions, with clear approval screens.
- Evaluate continuously. A test suite of real tasks runs on every change to prompts, tools or models.
- Trace everything. Each step and tool call is logged with cost and latency, so problems are easy to find.
Built on Aaga's Own Platform
Aaga builds on its own no-code and low-code platform, with modules for workflows, permissions, integrations and AI agents. Agents built on it get approval flows, role-based access, audit trails and admin screens without us writing them from scratch for each client. You pay for the logic that is unique to your business, not for plumbing. See the no-code platform case study for the platform's background.
AgentsHive: Our Work in the AI Agent Ecosystem
We built AgentsHive, a marketplace and community platform where users explore and compare AI agents by category, feature and business use case. It includes agent submission with admin moderation, lead collection for agent vendors and community discussions with AI-generated summaries. Building it gave our team a close view of how AI agents are packaged, evaluated and adopted by businesses.
Examples of AI Agent Use Cases
- Finance: match invoices to purchase orders, flag mismatches and prepare entries for approval.
- Sales operations: enrich new leads, update the CRM, draft personalized follow-ups and book meetings.
- Customer operations: triage support tickets, gather order and account data, and draft resolutions for review.
- HR and recruiting: screen applications against criteria, schedule interviews and answer policy questions.
- IT and engineering: watch logs and alerts, open tickets with diagnostics and run approved fixes.
- Compliance and documents: read contracts or reports, extract key terms and check them against your rules.
Where to Start
Agents pay off fastest on repetitive, rules-heavy work that still needs some judgment. If your process involves phone calls, see our AI voice agents. If it is mostly about generating or understanding content, our generative AI development page covers that. For broader process automation, see AI automation services, and for a side-by-side view, read AI agents vs chatbots.
Frequently Asked Questions
An AI agent is a software system that uses a large language model to decide what to do next, calls tools such as APIs, databases or other software, and keeps going until a task is done. It differs from a chatbot, which mainly answers questions, because it takes actions in your systems.
An autonomous agent completes a task end to end without asking anyone. A semi-autonomous, or human-in-the-loop, agent does the work but pauses for a person to approve key steps, such as sending a payment or emailing a customer. Most businesses start semi-autonomous and widen autonomy as trust grows.
The Model Context Protocol (MCP) is an open standard for connecting AI models to tools and data sources. We build MCP servers for your systems where it helps, so the same integrations can be used by different models and agents without rewriting them.
We choose per use case among models such as Claude, GPT, Gemini and open-source models like Llama. We use agent frameworks where they add value and plain, well-tested code where they don't. The goal is a reliable agent, not a particular framework.
We limit what each agent can access, add approval gates for sensitive actions, validate inputs and outputs, and test against real scenarios before launch. Every step is logged, so any error can be traced, fixed and added to the test suite.
Yes. Agents connect through APIs, databases, webhooks, email and file storage, or through MCP servers we build. For older systems without an API, we look at options such as database access, exports or browser automation.
A pilot agent for one well-defined workflow typically takes a few weeks. Multi-agent systems and agents that touch many systems take longer. After a short discovery call we give you a timeline and budget range for the pilot.
Put Your First AI Agent to Work
Pick one workflow. We'll design the agent, build a pilot on real data and show you the results before you commit to more.
Plan My AI Agent