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Autonomous AI agents that handle real work end-to-end - reasoning, acting, escalating.
Remote AI Agent DevelopmentA Dedicated DeveloperHourly or Monthly
AI agents that take actions, not just answer - planning, tool use, your APIs called and your systems updated, built by a remote dedicated developer for $25/hr or $2,000/mo, with a senior lead reviewing every release.
AI Agents From Empiric Infotech LLP
Last updated:
Empiric Infotech LLP builds custom AI agents that do work in your business - not chatbots that answer FAQs, and not no-code Zaps that fall over on the third step. Two ways to engage a remote dedicated AI agent developer: book hours at $25/hr for a defined scope (a v1 agent, an evals pass, a model swap), or lock a month at the standard $2,000 for 160-172 hours of full-time, exclusive work when the agent is a rolling thing with more tools, more flows, and patient iteration. Either way the developer works in your GitHub or GitLab org, your cloud (AWS, Azure, GCP, Hetzner, DigitalOcean, or Hostinger - whichever your team is on), and your model keys, with the orchestration framework that fits your case (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, the Anthropic SDK with tool use, or a hand-rolled state machine - chosen with you, not pushed). We design the agent's task and tool surface, wire it to your APIs and databases (directly or via an MCP server - see /services/mcp-server-development), build the prompt and tool-calling logic, add short-term memory and retrieval where it needs them, run it on the right trigger (cron, webhook, queue event, a Slack /command, an inbox watcher), and add evals, guardrails, retry/timeout/budget logic, approval gates, and an audit trail so a person stays in control. A senior team lead reviews and tests every release. Why the hourly premium? Agent work is high-iteration expert work - prompt and tool surface design, eval-driven testing, the long tail of edge cases as the agent meets real inputs; the monthly rate is the same flat $2,000 as any Empiric engagement once you commit. If your need is more conversational, see /services/chatbot-development; if it is a single repetitive workflow, see /services/ai-automation-services.
What an AI agent engagement delivers
Not a demo notebook that does one nice thing in a happy path. A production agent in your cloud, doing real work against your real systems, with the evals and guardrails that keep it from going off the rails.
An agent that plans and acts, not just answers
A multi-step LLM workflow that decides what to do next, calls your tools, reads results, and decides again - with short-term memory across a task, retrieval where it needs context, and a clear stopping condition. Built on LangGraph, CrewAI, AutoGen, the OpenAI Agents SDK, the Anthropic SDK with tool use, or a hand-rolled state machine, whichever fits the case. We will tell you when a single LLM call with tool use beats an agent for what you actually need.
Wired to your systems, not a sandbox
Adapters over your REST and GraphQL APIs, your Postgres or MongoDB, your CRM, your helpdesk, your email and Slack, your accounting and your internal services - exposed as agent tools with per-tool permission scopes, input validation, idempotency, and a single audited boundary. If you already have an MCP server, the agent uses it; if you do not, we can build one (see /services/mcp-server-development).
Evals, guardrails, and a human in the loop
Eval-driven testing of the agent on real inputs (regression on every prompt or tool change), retry and timeout and budget caps, content and safety filters where they matter, approval gates on actions with consequences (sending an email, charging a card, modifying a record), a clear escalate-to-human path, and an audit trail an oncall engineer or a compliance reviewer can read.
Runs on the trigger that fits the use case
Cron, webhook, queue event, Slack slash command, inbox watcher, a button in your admin UI, a row insert in your DB - whichever maps to how the work actually arrives. Production-grade scheduling, replay on failure, dead-letter handling, structured logs, and a dashboard on volume, success rate, latency, and per-run cost.
The right vertical agent for your case
We have built research agents (gather, summarise, cite), ops agents (triage, classify, route), sales-qualification agents (enrich, score, draft outreach), support agents with handoff (answer from your knowledge base, take actions, transfer when stuck), back-office agents (invoice processing, AP, reconciliation), and content agents (draft, fact-check, post). The shape is the same; the tool surface and the policy are yours.
Multi-agent only when it earns its complexity
A planner-worker pair, a critic-loop, a small team of specialised agents - useful when the task is genuinely long-horizon or genuinely needs separation of concerns. We will start with one agent and add more only when the eval numbers say it helps; we do not ship a five-agent system to look impressive.
A developer who is still there next month
Models change, your tools change, your edge cases change. A dedicated engagement means the same developer tunes the prompt, swaps the model, adds the next tool, and keeps the evals green, month after month - instead of handing you a v1 and a stale README.
How we scope an AI agent engagement
No multi-week sales cycle and no twenty-page statement of work. A call, a written scope, a trial, then hourly or monthly - your call.
A scoping call
Thirty to forty-five minutes. You tell us what work you want the agent to do (the task, the inputs, the outputs, the systems it should touch), how often it should run, who or what triggers it, and what would be a real, measurable outcome - hours saved, tickets deflected, leads qualified, errors caught. No charge, no obligation.
A written scope and team proposal
We send back the task definition, the tool surface and the trigger, the orchestration framework we would use and why, the evals we would measure against, the guardrails and human-in-the-loop points, the model and any rough cost-per-run estimate, who we would put on it - one developer to start, or a small dedicated team when the surface is large - and the price both ways. We will tell you honestly when a single tool-using LLM call beats an agent, or when an automation (see /services/ai-automation-services) is the better fit.
A 7-day risk-free trial (on the monthly plan)
The developer gets into your repo and cloud account and ships the first slice - the agent running end to end on a small, real slice of the task, with logs and an eval baseline, reviewed and tested by the senior lead - inside the first week. If it is not a fit by day 7, full refund on the monthly plan, no debate. On the hourly plan, you have already seen what an hour buys.
Hourly or monthly, your choice
Hourly: billed by the hour at $25, time tracked to the minute, a weekly time report and a demo, stop any time - best for a defined scope or a burst of work like an evals pass or a model swap. Monthly: 160-172 hours at the standard $2,000, monthly billing, cancel with 7 days notice - the better value when the agent is a rolling thing with more tools, more flows, and patient eval-driven iteration. Switch between them month to month as the work grows or settles; add a developer at the same rate.
Two ways to engage an AI agent developer
Two ways to engage a remote AI agent developer. By the hour at $25 - pay as you go, time tracked to the minute, a weekly report and demo, no monthly commitment - best for a defined scope like a v1 agent, an evals pass, or a model swap. Or monthly at the standard $2,000 for 160-172 hours of full-time, exclusive work - the better value when the agent is a rolling thing with more tools, more flows, and patient iteration, with a 7-day risk-free trial. Either way: your repo, your cloud, the right orchestration framework for the case, and a senior lead reviews and tests every release. Why the hourly premium? Agent work is high-iteration expert work - prompt and tool surface design, eval-driven testing, the long tail of edge cases; the monthly rate is the same flat rate as any Empiric engagement once you commit. Model usage (LLM tokens, embeddings, vector DB seats) is billed to your own accounts at cost.
Hourly plan
- A dedicated AI agent developer, exclusive to you while you have hours booked
- Pay as you go - billed by the hour, time tracked to the minute, a weekly report and demo
- Best for a defined scope (v1 agent, evals pass, model swap); no monthly commitment, stop any time
- Your repo, cloud, and model keys from day one
- Every release reviewed and tested by a senior lead before it goes live
Monthly plan
- A dedicated AI agent developer, full-time and exclusive - 160-172 hours a month
- The best value when the agent is a rolling thing - more tools, more flows, evals, model swaps
- Your repo and cloud from day one; the same flat rate as any Empiric engagement
- 7-day risk-free trial, monthly billing, cancel with 7 days notice
- A senior lead reviews and tests every release before it goes live
Dedicated team
- A small dedicated team - developers plus a senior team lead who reviews and tests every release
- Add a developer (or a designer for an admin UI) at the same rate, in 48 hours
- Pair an agent developer with an MCP server or chatbot developer to ship related surfaces at once
- Best for a multi-agent system, a multi-channel rollout, or running several agents in production
What the first 90 days of an AI agent engagement look like
Whether you are booking hours or on the monthly plan, the shape is the same. Here is a typical first three months on an agent build.
- Week 1
Onboarding and the first slice of the agent
Repo and cloud-account access, a working local environment, the orchestration framework chosen, the task and tool surface mapped, and a first slice live - the agent running end to end on a small, real subset of inputs, tools called against your real systems behind a permission scope, run logs, error alerting, and a first eval baseline - shipped and reviewed. On the monthly plan, day 7 is the risk-free decision point; on the hourly plan, you have seen what an hour buys.
- Month 1
An agent doing real work, in production
The priority task running end to end in production - your real triggers, your real tools, your real data - with the prompt and tool surface tuned, the human-in-the-loop gates working, retries and budget caps in place, a clear escalate path, an eval suite running on every change, and a dashboard on volume, success rate, latency, and per-run cost. By the end of month one a real chunk of work is off your team's plate or running faster.
- Month 2
Edge cases, more tools, the second use case
The edge cases month one surfaced - smoothed, with the prompt and tool surface tightened, the evals expanded, model-fallback handling for outages, more tools added (a second CRM action, a second data source, the next integration), and the second use case scoped or shipped. The agent stops being a v1 and starts being a system.
- Month 3 and on
Reliability, cost, and ahead of the roadmap
A reliability pass (retries, idempotency, dead-letter handling, replay on failure), a cost pass on per-run model and platform spend, a quality pass on the eval numbers and the model's decisions, a model swap if a newer or cheaper one wins on your evals, and the next agent (or the multi-agent step) scoped. From here the developer is ahead of your backlog - the next tool, the next flow, the next model.
A dedicated AI agent developer - hourly or monthly - vs a fixed-price AI agency, a no-code agent platform, or a freelance contractor
| Empiric Infotech (AI agent developer - hourly or monthly) | Fixed-price AI agency | No-code agent platform (configure-it-yourself) | Freelance contractor (Upwork, Fiverr, Toptal) | |
|---|---|---|---|---|
| What you actually get | A custom AI agent doing real work in your systems, owned by you, with the developer who built it still there to fix and extend it | An agent built to a spec, then a maintenance retainer or you are on your own | A dashboard and a generic agent you configure yourself; the edge cases and integrations are on you | One agent built, then they are gone; it rots when a model changes or a tool breaks |
| Pricing model | $25/hr for hourly work, or the standard $2,000/mo for a full-time developer if you lock a month - your choice; model and platform usage billed to your accounts at cost | $15K-$80K fixed bid for a v1 agent; change orders billed extra | Platform subscription ($50-$500/mo) plus your time configuring and maintaining it | $30-$150/hr, quality varies; scope creep comes out of your budget |
| Estimate before you commit | An estimate both ways - hours per use case or what a month covers - plus a weekly time report and a demo | A fixed bid - you wear the overage as change orders | Platform demos; the real cost shows up after week two of configuration | An hourly quote, often optimistic |
| Orchestration choice | The right framework for the case - LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Anthropic SDK with tool use, or hand-rolled - chosen with you | Usually whatever the agency standardises on, whether or not it fits | Whatever the platform supports - typically a visual editor over their runtime | Whatever the contractor knows |
| Evals and guardrails | Eval-driven from week one - regression on every prompt or tool change, content/safety filters, retry and budget caps, approval gates, an audit trail | Per the spec; new evals and gates are change orders | What the platform offers, often shallow; advanced cases are on you | Often skipped unless you ask |
| Quality control | A senior lead reviews and tests every release before it goes live - built in, no extra charge | Per agency - often the same people who built it | On you to review and verify | On you to review and verify |
| Who owns it | You - your repo, your cloud account, your data, your model keys, from day one | You, on final payment | You own the configuration; the runtime is the platform's | You per contract - check the IP-assignment clause |
| When the model changes (or breaks) | The same developer swaps the model, re-runs the evals, and ships the fix - book an hour, or it is in the monthly plan | A support ticket, or a new maintenance retainer | Wait for the platform to support it; in the meantime you are stuck | Re-hire the contractor, or it stays broken |
| Time to start | 48 hours | 2-6 weeks (proposal, SOW, kickoff) | Days (sign up); weeks of configuration before it does real work | Days to weeks (post a job, review, interview) |
Figures are typical market ranges, not quotes. Platform and model usage costs (LLM tokens, embeddings, vector DB seats) apply on top of any build cost in every option and are billed to your own accounts in ours. A fixed-price agency build of a comparable agent commonly lands in the $15K-$80K range before change orders, depending on the tool surface and the number of use cases.
Working hours and meeting availability
Our AI agent developers work 09:30 AM to 07:30 PM IST, Monday to Friday. A project manager is reachable 07:30 AM to 10:30 PM IST. Live overlap by region:
| Region | Developer live overlap | PM available for meetings | What this means |
|---|---|---|---|
| USA East (ET) | 1 hr 9:00-10:00 AM ET | 9:00 PM previous day - 12:30 PM ET | Morning standup, then most of a working day's prompt and tool-surface work shipped async before your day starts. |
| UK and Ireland (GMT/BST) | 5-6 hr 9:00 AM - 2:00 PM | Full UK working day | Live eval reviews, prompt walkthroughs, and watching a run together across the morning. |
| Western Europe (CET/CEST) | 6-7 hr 9:00 AM - 4:00 PM | Full CET working day | Effectively a same-time-zone working relationship - live design, pairing, and review through most of the day. |
| Sydney and Melbourne (AEST/AEDT) | 3.5 hr 2:00 - 5:30 PM AEST | 12:00 noon - 3:00 AM next day AEST | Afternoon standup and live review, then overnight async builds, evals, and deploys. |
Why teams build their AI agent with a dedicated developer, not a fixed-price agency
What gets shipped in production AI agents is rarely the happy path that lit up the demo, it is the long tail: a flaky tool that times out on the third call, an LLM that loops on a bad plan, a retry that double-charges a card, a guardrail that has to hold the day a vendor rotates a model. An in-house engineer who has lived that loop is roughly $9,200 to $13,300 a month once you add benefits, payroll tax, and equipment, and a strong AI-fluent one is hard to hire and slow to start. A fixed-price AI agency build of a v1 agent typically runs $15,000 to $80,000 before the first change order, then a separate maintenance retainer. Empiric Infotech is billed two ways - $25 an hour for a defined scope, or the standard $2,000 a month per developer for 160-172 hours of full-time, exclusive work - with the same person on your agent the next month and the month after, and a senior lead reviewing and testing every release at no extra cost.
AI agent work is recent enough that the risk on a fixed-price build is real: an agency learning the orchestration framework on your dime, an agent that does the happy path and falls over on edge cases, evals that exist only in the demo, no guardrails or human-in-the-loop, a v1 delivered the day the SOW closes and then frozen while the model lineup shifts. A dedicated Empiric developer has shipped AI agents - tool use, retrieval, multi-step planning, evals, guardrails - in production for B2B SaaS, ops, and product teams, and is still there next month when the model improves or your tool surface grows.
We have built AI and LLM features into products since the current wave began - retrieval, agents, structured extraction, model integration - and shipped web and mobile products since 2014. The depth shows up in the parts a quickstart skips: tool surfaces modelled to your domain, idempotent tool calls so a retry does not double-charge a card, evals that catch a regression before your users do, a human-in-the-loop boundary that an oncall engineer trusts, and the honesty to say when a single tool-using LLM call (or an automation) beats an agent for what you are actually trying to do.
Recent AI, product, and integration work
Superintelligence - an AI product engineered end to end
An AI product built for a USA client by an Empiric Infotech team - the application, the model integration, and the backend behind it. The kind of LLM-and-tooling work an agent build sits inside.
Read case studyVedu - AI image generation and TTS in an EdTech app
An AI-powered German language learning product built for a German client by a two-person Empiric Infotech team - the Flutter app, the flashcard and quiz engine, text-to-speech, and AI image generation wired into the content backend. The integration and evals discipline that an agent build needs.
Read case studyRoamate - AI features inside a travel platform
A solo travel companion platform built for a USA founder by a two-person Empiric Infotech team - the Flutter app, the web surface, real-time chat, and the APIs and AI-assisted features behind them. Agent-style assistance inside a real product.
Read case studyReady to build your AI agent?
Tell us what work you want the agent to do - the task, the inputs, the outputs, the systems it should touch, how often it should run, and what would count as a real outcome. Within 24 hours we will send back a task definition, a tool surface and trigger, the orchestration framework we would use, an evals plan, a team proposal, and an estimate both ways - hours per use case or what a month covers. Your developer starts inside 48 hours, and a senior lead reviews and tests everything before it reaches you.
Who We Help:
For Teams Who’ve Outgrown
Chatbots and Templates
We help engineering, operations, and product teams replace brittle chatbots and manual tasks with AI Agents - designed for scale, reliability, and seamless integration with your real business processes.
This Is for You If:
You're tired of generic bots that miss nuance and break under pressure
You need agents that understand your workflows and data
You're managing fragmented support, sales, or operations manually
You're looking to reduce response time, errors, and burnout
You want custom logic, fallback handling, and full control over AI behaviour
What We Do
AI Agents That Handle Real
Operations - Not Just FAQs
We build domain-specific agents that go beyond answering questions. They reason, decide, and act across workflows and channels.
What We Build:
Autonomous sales agents for inbound lead response
AI support agents for triage, follow-up, and ticket generation
Voice-enabled assistants for IVR and scheduling
Internal ops agents for reporting, data cleaning, and task automation
Multilingual, multi-channel agents (email, chat, WhatsApp, voice)
Built With:
We’re platform-agnostic - if it can be automated, we’ll make it happen.
Where AI Agents Make a Measurable Impact
Sales & Lead Response
Instant replies, qualification, calendar booking - without human delay
Built with: n8n, Make, Zapier, and custom API orchestration
Customer Support
Triage queries, update CRMs, generate tickets, escalate intelligently
Powered by: Chat-GPT, LangChain, OpenAI, Gemini
Voice IVR Agents
Smart, conversational IVRs that solve, not just redirect
Tech behind the scenes: OCR, LLMs, vector search, Pinecone
Internal Operations
Generate reports, clean data, run daily workflows on command
Built using: Twilio, Vapi, Retell AI, Telegram, OpenAI
Knowledge Retrieval
Instantly surface SOPs, docs, and product info with citations
Integrated into: Notion, Data Studio, PowerBI, and more
Our Step-by-Step Process for AI Agent Development at Scale
A Strategic Process to
Build Agents That Think Before
They Talk
Every AI agent we build is grounded in your real data, use case, and operations - not just an LLM prompt.
Discovery & Use Case Mapping
Identify high-impact areas for automation and conversational UX
Outcome: A ROI-driven roadmap aligned with your business goals
Prompt & Agent Design
Craft system-level prompts, memory design, and fallback handling
Outcome: A flexible, future-ready blueprint with no platform lock-in
LLM & Stack Selection
Choose best-fit LLMs, APIs, and hosting models for your needs
Outcome: Quick wins and stakeholder buy-in through targeted POCs
Development & Integration
Build, train, and connect agents into your tools and workflows
Outcome: Fully functional, end-to-end automations in your environment
Testing, Training & Handoff
Validate edge cases, train your team, and iterate based on feedback
Outcome: Full ownership, independence, and a trained internal team
Industries We Build Agents For
AI Agents, Built for the Demands of Your Sector
Whether you’re an agile startup or a regulation-heavy enterprise, our bespoke AI agents streamline complex workflows, unify disparate systems, and automate repetitive tasks-fully tailored to the unique challenges and compliance needs of your sector.
Industries We Serve:
SaaS & Tech Startups
Automate GTM workflows, onboarding, CRM updates, and support escalations.
E-commerce & DTC Brands
Streamline order processing, inventory sync, customer notifications, and returns.
HR & Recruitment Teams
Simplify candidate screening, ATS syncing, and onboarding checklists.
Healthcare Admin & Support
Automate patient intake, scheduling, and compliance tracking securely.
Logistics & Operations Teams
Route orders, sync inventory, trigger dispatch workflows - all in real time.
EdTech Platforms
Automate testing, grading, learner onboarding, and progress updates.
If your operations feel too complex for templates, you're in the right place.
Why Empiric for AI Agents
More Than Chatbots - We Build Thinking Systems
We don’t do off-the-shelf bots or one-size-fits-all workflows. Instead, we architect bespoke, scalable AI agents that integrate deeply with your existing operations and evolve alongside your business.
What Sets Us Apart:
Deeply Integrated with Your Operations
We architect AI agents that connect to your databases, APIs, and legacy systems-no generic, one-size-fits-all logic.
Privacy-First, Self-Hosted Options
Custom AI agents built to your security standards-GDPR-compliant and deployable on your infrastructure.
Stateful Agents with Robust Fallbacks
Agents retain context, handle errors gracefully, and expose full observability so you always know what’s happening.
Rapid v1 Builds & Real-World Testing
We deliver working prototypes fast, validate them with live data, and iterate to maximize ROI and stakeholder buy-in.
Full Ownership of Logic & Data
You get the complete code, configurations, and datasets-no black boxes, no vendor lock-in, just transparent AI.
Continuous Learning & Optimization
Agents continually learn from interactions and feedback, adapting their behavior to improve accuracy and performance over time.
Tools We Integrate - Built Around Your Stack, Not Ours
Agnostic. Conversation-First Aligned to Your Stack.
We architect AI agents as the central intelligence in your ecosystem, plugging into your existing data sources, APIs, and workflows. Whether you’re leveraging open-source models, enterprise platforms, or a hybrid stack, our solutions adapt to your tech environment-delivering a unified, scalable system without forcing you into a one-size-fits-all toolchain.
What We Work With:
AI & Language Models
Automation Platforms
AI Frameworks
Voice & Communication
Backend & Database
OpenAI
Claude
Gemini
Mistral
Meta LLaMA
We’ll design a setup that fits your compliance and control needs.
Why Businesses Choose Empiric Infotech LLP?
We worked with Empiric Infotech to build a chatbot for our children’s theater project, and we’re so glad we did. The team was fast, responsive, and kept working until everything was perfect. The project was delivered on time, within budget, and the end result looks and works great. We’re truly grateful for their support
E-commerce Brand
I found in Empiric Infotech an excellent partner to build my blockchain platform. They are professional, knowledgeable, and supportive at every stage. Even after launch, we keep collaborating - and the results have always been wonderful
Founder Winupdraws
I was looking for a skilled software team to help me transform my prototype into a complete app. Empiric Infotech not only delivered exactly what I needed, but also added custom features, backend notifications, and guided me through publishing on both app stores. Their communication was smooth and reliable. I can gladly recommend them.
Founder and consultant at tak innovation
We needed support to strengthen our technical platform, and Empiric Infotech delivered exactly what we were looking for. The team was professional, responsive, and kept everything on track with both time and cost. As our company grows, we’re glad to have them as a trusted partner and would happily recommend their services.
Founder Skapasaga
Finding the right agency for our My Ayur app was challenging, but Empiric Infotech delivered exactly what we needed. Over past months, their expertise in FlutterFlow and MongoDB stood out, consistently delivering high-quality work. Professional, friendly, and reliable - a partner I would confidently recommend.
Founder and CEO Sresht Ayur
We worked with Empiric Infotech to build a chatbot for our children’s theater project, and we’re so glad we did. The team was fast, responsive, and kept working until everything was perfect. The project was delivered on time, within budget, and the end result looks and works great. We’re truly grateful for their support
Eva MesmanE-commerce Brand
I found in Empiric Infotech an excellent partner to build my blockchain platform. They are professional, knowledgeable, and supportive at every stage. Even after launch, we keep collaborating - and the results have always been wonderful
John FelipeFounder Winupdraws
I was looking for a skilled software team to help me transform my prototype into a complete app. Empiric Infotech not only delivered exactly what I needed, but also added custom features, backend notifications, and guided me through publishing on both app stores. Their communication was smooth and reliable. I can gladly recommend them.
Andrzej KarelFounder and consultant at tak innovation
We needed support to strengthen our technical platform, and Empiric Infotech delivered exactly what we were looking for. The team was professional, responsive, and kept everything on track with both time and cost. As our company grows, we’re glad to have them as a trusted partner and would happily recommend their services.
Fredrik HagenFounder Skapasaga
Finding the right agency for our My Ayur app was challenging, but Empiric Infotech delivered exactly what we needed. Over past months, their expertise in FlutterFlow and MongoDB stood out, consistently delivering high-quality work. Professional, friendly, and reliable - a partner I would confidently recommend.
Eshu MiddhaFounder and CEO Sresht Ayur
Compliance & Security
AI Agents Without Compromising Privacy or Control
What We Deliver:
GDPR-compliant conversational and data workflows
Role-based access controls (RBAC) for sensitive interactions
Self-hosting options for all LLMs, vector stores, and agents
Full audit trails, logging, and data retention policies
Built for teams that need smart agents - and strict compliance.
Let’s Build the Smart System Your
Business Deserves
FAQs
Answers to Common Questions - From Founders, Ops Teams & Tech Leads
Frequently asked questions
What is an AI agent and how is it different from a chatbot?
How much does AI agent development cost - hourly or monthly?
Which orchestration framework do you use - LangGraph, CrewAI, AutoGen, or something else?
How do you keep an agent from going off the rails?
Can the agent use my existing MCP server (or one you build)?
Do I need a multi-agent system, or one agent?
Which models do you use - Claude, GPT, Gemini, open-source?
Where does the agent run, and who owns it?
How fast can you start, and what if it is not working out?
How is this different from your AI automation or chatbot services?
GET A QUOTE NOW
Tell us about your challenges, and we’ll come up with a viable solution!
