AI agent development company for production systems
We design and build AI agents that call your tools, read your data and complete real workflows, with evaluation, guardrails and human approval built in from the first sprint.
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MappOptimist is an AI agent development company that provides AI agent development services: custom agents and multi-agent systems for enterprises and SaaS teams. We handle tool calling, orchestration, RAG grounding, evaluation, guardrails and deployment into your stack. Our India-based engineering team delivers to clients worldwide as a fixed build, a managed pod, a dedicated team or individual LLM and agentic engineers.
Our AI agent development services
AI agent development services cover everything needed to put an agent into production: scoping the workflow, building the tools and orchestration, grounding answers in your data, connecting the agent to the systems you already run, testing it with an evaluation suite and keeping it reliable after launch. We provide each of these as a fixed build, a managed pod, a dedicated team or individual LLM and agentic engineers, at published hourly rates.
Agent scoping and architecture
We map the workflow, the systems the agent must touch, the data it may see and the actions that need human sign-off, then design the agent graph, tools, retrieval, memory and approval gates as one blueprint, with an evaluation set built from your real examples.
Custom tool-calling agents
Agents that act through typed, permissioned tools with structured outputs, retries and timeouts, orchestrated with LangGraph or LangChain so every step, branch and hand-off is explicit and testable.
Multi-agent systems
Specialised agents for distinct jobs coordinated by a planner or a state graph. For a logistics and e-commerce enablement platform, our tracking, support and payments agents automated 65% of repetitive support tasks.
RAG and knowledge agents
Agents that answer from your policies, product documents and live records through retrieval over vector stores such as Pinecone, Weaviate or Qdrant, and cite their sources so reviewers can check them.
Agents inside the systems you already run
Integration with your CRM, ticketing, scheduling and records systems. Examples include AI agents that coordinate scheduling, records and workflows for a US hospital network, and Dynamics 365 with Copilot and Azure AI for a technology company.
Evaluation, guardrails and support after launch
Scenario suites that run on every change, input and output guardrails, escalation to a person, tracing of every run, and ongoing tuning of prompts, models, retrieval and cost once real usage data arrives.
Example: for a logistics and e-commerce enablement platform, our multi-agent support system with agents for tracking, support and payments automated 65% of repetitive tasks and cut response times by 40%. Read the support agents case study.
Choosing an AI agent development company
An AI agent development company designs the tools an agent may use, the orchestration that plans its steps, the retrieval that grounds its answers, the evaluation that proves it works and the guardrails that stop it doing harm, then runs it in production. When you compare companies, check the six points below. This is how we answer each of them, including for teams in the US and UK.
Agents in production, not only demos
Ask for agents that act inside real systems. Ours include an agentic AI platform for an enterprise SaaS company in the UK, AI agents that coordinate scheduling, records and workflows for a hospital network in the US, and a multi-agent support system that automated 65% of repetitive tasks for a logistics and e-commerce platform.
An evaluation suite before launch
A serious partner builds test scenarios from your real examples before writing agent logic, and runs them on every change to prompts, models or tools, so regressions show up before users see them.
Permissioned tools and human approval
The agent should only reach your systems through typed, permissioned tools, with consequential actions such as refunds or record changes paused for a person to approve and every step logged.
Costs you can see before you start
Our AI engineering rates are published by role and experience band, and our AI agent development cost guide shows what a pilot, a production team and a multi-agent platform cost per month.
Who you deal with
Your first conversation is with our founder, who runs delivery, technology decisions and client relationships directly, not handed off after the sales call.
Working hours for US and UK teams
Our engineers work in your time zone for US and UK clients, joining your standups and releases. Start with a fixed build, a managed pod, a dedicated team or one senior agentic engineer.
Example: for a US hospital network, our AI agents coordinate scheduling, records and workflows, with staff approving the decisions that matter. Read the hospital agents case study.
Problems we solve
The demo works, production doesn't
A prototype agent answers well in a notebook, then loops, calls the wrong tool or invents a field when it meets real users and messy data. Nobody can say why it failed.
Chat is not the same as getting work done
Off-the-shelf assistants can talk, but they cannot be trusted to act inside your CRM, ticketing or payment systems without permissions, approvals and an audit trail.
Answers are not grounded in your data
Policies, product docs and live records sit across several systems. Without retrieval and source citations, the model fills the gaps with plausible guesses.
Support volume grows faster than the team
Repetitive queries such as tracking, status and payments take up most of the day, leaving little time for the cases that genuinely need a person.
Token costs and latency are unpredictable
Every agent step is a model call. Without routing, caching and limits, a busy week can multiply the bill and slow responses beyond what users accept.
What we build
Tool-calling agents on governed integrations
Each tool is a typed, permissioned API wrapper with structured outputs, retries and timeouts. Tools can also be exposed through MCP servers so several agents share one governed interface. The agent can only do what it has been explicitly granted.
Multi-agent orchestration
Specialised agents for distinct jobs, such as tracking, support and payments, coordinated by a planner or a state graph. We use LangGraph or LangChain so every step, branch and hand-off is explicit and testable.
RAG development and grounding
Ingestion, chunking, embeddings and hybrid search over vector stores such as Pinecone, Weaviate or Qdrant, plus real-time retrieval from live systems. Answers cite their sources so reviewers can check them.
Memory and session state
Short-term session memory for multi-turn, multi-user conversations and longer-term memory for user preferences and case history, stored where your data-retention rules allow.
Evaluation, guardrails and observability
Curated scenario suites run before every release to catch regressions. Input and output guardrails, PII handling and escalation rules sit around the model, and every plan, tool call and output is traced and replayable.
LLM integration and cost control
Integration with OpenAI, Claude, Gemini or open-source models via Hugging Face. Model routing by task difficulty, caching, token budgets and step limits keep latency and spend inside agreed bounds.
How an engagement runs
- Step 01
Discover
We map the workflow, the systems the agent must touch, the data it can see and the actions that need human sign-off. We agree what success looks like and how it will be measured.
- Step 02
Architect
We design the agent graph, tool registry, retrieval layer, memory, guardrails and approval gates as one blueprint, and build the evaluation set from real examples before writing agent logic.
- Step 03
Engineer
Senior engineers build in short iterations against the evaluation suite, with tracing on from day one. Each release is tested on curated scenarios before it reaches users.
- Step 04
Deploy and optimise
We deploy into your cloud with Docker and Kubernetes, monitor quality, latency and cost in production, and tune prompts, models and retrieval as real usage data arrives.
Ways to work with us
Fixed build / managed pod
We scope, build and ship a defined agent system end to end, or run a managed pod against outcomes you set. Milestones, a senior delivery team and a production-ready handover.
Dedicated team
A cross-functional squad of LLM engineers, agentic developers and an AI solutions architect who work in your standups and own delivery alongside your team on a sustained roadmap.
Individual experts
A single LLM engineer or agentic systems developer who joins your existing team on a monthly rolling basis. You interview and approve everyone before they start.
Need individual engineers rather than a project? See the roles you can hire and their rates.
Technology we work with
Case studies
Agentic AI Platform
Goal-driven AI agents that plan, call tools and complete real workflows under human oversight.
Read the case study →AI Agents · SupportAutonomous support agents
A multi-agent customer support chatbot, with specialised bots for tracking, support and payments, that automates repetitive queries and frees the team for complex issues.
Read the case study →AI Agents · HealthcareHospital Management Agentic System
AI agents that coordinate hospital scheduling, records and workflows under staff control.
Read the case study →AI / ML · LogisticsLogistics Management Bot
Intelligent tracking, routing and exception handling across the logistics chain.
Read the case study →Dynamics 365 + Copilot · TechnologyAI-powered customer engagement
Dynamics 365 supercharged with Microsoft Copilot, Power Platform and Azure AI, cutting admin and lifting sales productivity for a fast-growing technology company.
Read the case study →Frequently asked questions
What AI agent development services do you offer?
We offer agent scoping and architecture, custom tool-calling agents, multi-agent systems, RAG and knowledge agents, integration of agents with your CRM, ticketing and records systems, and evaluation, guardrails and support after launch. You can engage us for a fixed build, a managed pod, a dedicated team or individual LLM and agentic engineers on a monthly rolling basis.
Do you build AI agents for companies in the US and UK?
Yes. Our published AI agent work includes an agentic AI platform for an enterprise SaaS company in the UK and AI agents for a hospital network in the US. We deliver remotely from New Delhi with engineers working US or UK business hours, and your first call is with our founder.
What does an AI agent development company actually build?
An agent is an LLM that plans steps toward a goal and acts through tools, such as APIs, databases or internal services, rather than only replying in text. We build the tool layer, orchestration, retrieval, memory, guardrails, evaluation suite and deployment. For a logistics and e-commerce enablement platform, our multi-agent support system automated 65% of repetitive tasks and cut response times by 40%.
What drives the cost of building an AI agent?
The main drivers are the number of systems the agent must integrate with, how many actions need approval flows, the size and quality of the data behind retrieval, evaluation depth, compliance requirements and expected traffic, which sets model spend. On our AI rate card, an Agentic Systems Developer is $38, $45 or $60 per hour by experience band, and an LLM Engineer $40, $48 or $62. Rates are indicative and confirmed per scope. Our AI agent development cost guide shows example team costs per month.
Should we build a custom agent or buy an off-the-shelf product?
Buy when the workflow is generic, such as basic FAQ deflection, and the vendor already integrates with your tools. Build when the agent must act inside your own systems, follow your approval and audit rules, keep data in your cloud, or combine several specialised tasks. Many teams start with a bought product and move to a custom build once they hit its limits.
How do you stop an agent from taking a wrong or harmful action?
Agents only reach systems through permissioned tools with typed inputs. Consequential actions, such as refunds or record changes, pause for human approval. Guardrails check inputs and outputs, low-confidence or sensitive cases escalate to a person with full context, and every step is logged so it can be reviewed, replayed and, where required, reversed.
How do you evaluate an AI agent before it goes live?
We build a scenario suite from real examples: expected answers, correct tool choices and cases where the agent should refuse or escalate. Every change to prompts, models or tools runs against it before release. In production we trace each run and track quality, latency and cost, so regressions show up in dashboards rather than in customer complaints.
Which frameworks and models do you use?
Our default stack is Python with LangChain or LangGraph for orchestration, LlamaIndex for retrieval pipelines, and Pinecone, Weaviate or Qdrant as vector stores. We integrate OpenAI, Claude and Gemini APIs or open-source models from Hugging Face, and pick the model per task on accuracy, latency, cost and where your data is allowed to go.
Can you add engineers to our existing AI team instead of running the project?
Yes. We place individual LLM engineers, agentic systems developers, MLOps engineers and AI solutions architects into your team on a monthly rolling basis, typically onboarded in about a week. They work in your tools, repositories and standups, and you interview and approve each person before they start.
Related services
Scope your AI agent
Tell us the workflow, the systems involved and where a person must stay in the loop. We will come back within one business day with an architecture view and the best place to start.