AI Agent Development

Agents That Do the Work, Not JustAnswer the Question

Chatbots answer. Agents act. We build AI agents that plan multi-step work, call your tools and APIs, hand off to humans when they should, and survive production load — using LangGraph, AutoGen, Azure AI Foundry Agents, OpenAI Assistants, and custom orchestration. Scoped to real workflows, evaluated continuously, and governed so one bad decision can’t brick a business process.

Where Agent Projects Stall

Your LangChain demo works in a notebook. In production it loops, times out, or hallucinates a tool call that doesn’t exist.

You scoped an “autonomous agent” for a business process. Nobody agreed where the human stays in the loop.

Your multi-agent system runs five LLM calls per task. Token cost is three times what you forecast.

An agent failed silently and took a downstream action that shouldn’t have happened. Your team found out days later.

Model updates change agent behaviour. You don’t have regression tests for agent trajectories, so you can’t tell.

Where Are You Starting From?

Need a single-purpose agent that automates a specific workflow

Task-Specific Agent Build

Multi-agent system — planner, executor, reviewer

Multi-Agent Architecture

Agent needs to call our APIs and tools (function calling)

Tool-Use & Function Calling

Moving an agent PoC from notebook to production

Agent Productionisation

Need guardrails, human-in-the-loop, and audit trail

Agent Guardrails & Governance

Existing agent is slow, expensive, or unreliable

Agent Optimisation

Evaluating LangGraph vs AutoGen vs Azure AI Foundry Agents

Framework Selection

Agentic data pipelines / analytics agents

Data Agents

What can I help with ?

What Changes After We Engage

Six scenarios the buyer can picture.

Agents complete real workflows, not just answer questions

A procurement request lands. The agent gathers the supplier quotes, checks budget, drafts the PO, and hands it to the approver with a summary. The procurement team closes the loop in 20 minutes, not three days.

Humans stay in the loop where they should

High-stakes steps (irreversible actions, large spend, customer-facing decisions) route to a human reviewer. Low-stakes steps run autonomously. The split is defined in the agent graph, not left to the LLM's judgement.

Token cost tracks per completed task

Your dashboard shows cost per successful agent run. Runaway loops get caught by step limits. Cost-per-outcome is a business metric, not a surprise at the end of the month.

Regression tests catch agent drift

Anthropic releases a new model version. Your agent trajectory tests run against it. Regressions get flagged before the version gets promoted. Agent behaviour stays predictable across model updates.

Guardrails stop the bad outcomes that scare the board

Input validation, output checking, tool-call allow-lists, rate limits, and kill switches are part of the architecture. An agent can't take an action outside its authorised scope, even if the LLM tries.

New use cases reuse the agent platform

The second agent ships in a quarter of the time because the orchestration, evaluation, guardrails, and monitoring are already there. Your team extends the platform, not rebuilds it.

How We Engage

1

Scope the agent and the human handoff

We map the workflow step by step, mark which actions run autonomously and which need a human, define tool-use scope, and agree kill-switch criteria before we start building.

2

Build with evaluation and guardrails from day one

We build the agent graph on LangGraph, AutoGen, or Azure AI Foundry depending on fit — with trajectory tests, guardrails, monitoring, and cost tracking wired in before the agent touches real data.

3

Handover with runbooks and a platform to extend

You walk away with a documented agent platform, trajectory evaluation suite, cost and monitoring dashboards, and patterns your team can reuse for the next three agents without rebuilding scaffolding.

Ship an AI Agent That Completes the Task — Not One That Demos Well

Book an AI agent scoping call. We’ll map the workflow, define the human handoff points, pick the framework, and come back with a build plan that gets the agent to production in weeks, not quarters.

Frameworks & References

Agent Frameworks

LLM Platforms

Tool Use & Protocols

Evaluation & Monitoring

Vector & Retrieval

Guardrails & Safety

Microsoft Partner Stack

What Clients Say About Working With Exillar

Excellent work as always by Umair and team. Umair and team continue to provide excellent work product. Highly recommend, responsive and attention to detail. Umair + Exillar team continue to impress and innovate as business needs evolve

Thanks for the project. If you are an Executive, you need a PowerBI dashboard. Great working with the team. Many ongoing projects with Umair. Great person to work with.

These guys are true professionals, they helped me improve the idea of the work I wanted to develop, very kind and prepared. We will definitely do more work together. second work and I’m very statisfied

The guys were great to work with, very fast to reply and have a deep understanding of PowerBI. This become a learning experience for me as they shared best practices for PowerBI.

Thanks for the exceptional work!

It was a great experience.

Umair handled my problem timely and efficiently. He is easy to collaborate with and I will be using him again.

Super good explanation, patience and a good sense of indagatory about the data, sources, etc. The solutions suggested were very safisfactory.

It is always a pleasure to work with Umair and count on his skills to assist us. I highly recommend him. He has excellent communication skills, which makes my life much easier when conveying out needs to a plan, and executing it.

Honestly, this has been an outstanding experience from start to finish.The team went far beyond my expectations — not only did they understand a very complex real-world operation, but they were also able to translate it into a functional and well-structured system.

Working with Exillar has been amazing. Bhavisha has has gone above and beyond to get us what we need. Very pleased. ~Sherwin

It is always a pleasure to work with Umair and his team. Rock start service!

Industries We've Worked In

Got Questions?

What's the difference between an AI agent and an AI chatbot?

A chatbot answers questions. An agent takes actions — calls APIs, updates systems, completes multi-step workflows. A chatbot ends at a response; an agent ends at a completed task. Different architecture, different evaluation, different guardrails.

LangGraph vs AutoGen vs CrewAI vs Azure AI Foundry Agents — which should we use?

LangGraph for explicit state-machine control over trajectory. AutoGen and Semantic Kernel for Microsoft-heavy stacks with Azure AI Foundry integration. CrewAI for role-based multi-agent patterns. We pick per your existing stack, team skill, and workload profile.

How do you stop an agent from running away or making a bad decision?

Four layers: step and cost limits on every run; tool-use allow-lists (the agent can only call approved APIs); human-in-the-loop at defined high-stakes steps; output validation and content safety. No single layer is enough; agents in production need all four.

How is cost controlled in a multi-step agent?

Per-run step limit, per-run token budget, model selection per step (cheaper model for routing, premium model for reasoning), prompt caching, and structured outputs. Cost per completed task gets tracked in the monitoring dashboard alongside success rate.

Agents That Do the Work, Not JustAnswer the Question

Where Agent Projects Stall

Where Are You Starting From?

What Changes After We Engage

Agents complete real workflows, not just answer questions

Humans stay in the loop where they should

Token cost tracks per completed task

Regression tests catch agent drift

Guardrails stop the bad outcomes that scare the board

New use cases reuse the agent platform

How We Engage

1

2

3

Ship an AI Agent That Completes the Task — Not One That Demos Well

Frameworks & References

What Clients Say About Working With Exillar

D&K

Growloup

willybesmart

Darcy

Hans

Miguel

Travis

Raul Rodriguez/F&K

Alex

Latamsa

Loudermilk Homes

Alex

Industries We've Worked In

Retail & E-Commerce

Healthcare

Finance & Banking

Real Estate & Construction

IoT & Technology

Manufacturing & Industrial

Got Questions?

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