Chatbots answer questions. Agentic AI does the work. An AI agent perceives a situation, makes a plan, and executes it across your business systems — checking the CRM, drafting the email, updating the record, notifying the team — then reports back. We design and build production agentic AI systems for businesses that want automation beyond the chat window.
What agentic AI does differently
A chatbot’s world ends when the conversation ends. An agent’s work begins there. Give an agent a goal — “follow up every quote older than 7 days” — and it pulls the quote list from your CRM, checks which ones went cold, drafts personalized follow-ups in your tone, sends them, logs the activity, and flags the hot replies for your sales team. That’s not a demo. That’s a Tuesday.
The difference that matters for your business: agents compound. Every workflow you hand to an agent keeps running — nights, weekends, holidays — while your team does the work only humans can do.
Agentic AI systems we build
- Customer support agents — resolve tickets end-to-end: look up the order, check the policy, issue the refund or replacement, and reply. Escalate to humans only when judgment is genuinely needed.
- Sales and research agents — enrich inbound leads, score them against your ICP, draft outreach, and book meetings directly into your calendar.
- Operations agents — monitor inventory, chase invoices, reconcile data between systems that were never designed to talk to each other.
- Knowledge agents — grounded in your documents (RAG), answering staff and customer questions with cited, accurate answers — never hallucinations presented as fact.
Our process
- 1. Workflow audit. We map the multi-step workflows eating your team’s hours and score them for agent suitability: clear inputs, definable “done”, and acceptable error cost.
- 2. Agent design. We define the agent’s tools, permissions, and guardrails — especially human-approval steps for consequential actions. An agent that can email customers gets oversight by design, not as an afterthought.
- 3. Build and integrate. We connect the agent to your stack — CRM, email, databases, APIs, or n8n workflows where no API exists — using current LLM platforms (OpenAI, Claude, open models where they fit).
- 4. Supervise and tighten. Agents go live with monitoring. We review what they did, tighten instructions, and expand their responsibilities only as trust is earned.
Why MTD for agentic AI
Most “AI agencies” sell demos. We ship integrations — our AI integration work already connects LLMs to real business systems, so our agents start from working infrastructure, not slideware. We tell you honestly when a workflow needs a person, when a chatbot is enough, and when an agent will actually pay for itself.
Frequently asked questions
What is agentic AI in simple terms?
Agentic AI is software that completes multi-step tasks on its own: it perceives a situation, plans the steps, and uses tools (your CRM, email, databases, APIs) to execute them — then reports what it did. Unlike a chatbot, it takes actions, not just gives answers.
How much does agentic AI development cost?
A focused single-workflow agent typically runs $5,000–$15,000 depending on how many systems it touches and what guardrails it needs. Multi-agent systems are scoped individually after a workflow audit.
Is agentic AI safe for customer-facing work?
Yes — when built with guardrails. We put human-approval steps on consequential actions (refunds, outbound emails, record changes), ground answers in your documents, and monitor everything the agent does, especially in the first months.
What’s the difference between an AI agent and a chatbot?
A chatbot holds conversations. An AI agent executes workflows across your systems. If the job ends when the conversation ends, you need a chatbot. If follow-up work spans multiple tools and steps, you need an agent.
Which AI models do you build agents on?
We work with leading LLM platforms — OpenAI, Claude, and open-source models where data control or cost demands it. Model choice follows the workflow’s needs: reasoning depth, tool-use reliability, and operating cost.
Your competitors are experimenting with agents. The winners will be the ones who deploy them on real workflows first. Tell us the workflow eating your team’s hours — we’ll tell you honestly whether an agent is the right tool, and what it would take to build one.
Exactly what you get in an agentic AI build
Agentic systems are not chatbots with extra steps — they’re goal-directed software. Every build includes:
- Goal & scope definition: what the system is allowed to do autonomously, and where humans stay in charge — written down before we build.
- Multi-step workflow design: plan → act → verify loops, with explicit success criteria for each step.
- Tool-use integration: your APIs, databases, and SaaS tools wired as agent tools with scoped permissions.
- Memory & state design: short-term context plus persistent memory where the task spans sessions.
- Guardrails: action allowlists, spending/rate limits, and human approval gates on irreversible actions.
- Evaluation harness: test scenarios the agent must pass before production — and re-pass after every change.
- Monitoring & audit trails: every agent action logged with its reasoning, reviewable by your team.
- Documentation & handover: how the system works, its limits, and how to extend it.
Agentic AI development pricing
Fixed quotes after a free scoping call. As a guide: a focused single-domain agent (research, triage, drafting with tools) typically runs $4,000–$10,000. Multi-agent systems with complex tool use and guardrails run $10,000–$30,000+. Ongoing model usage is billed separately and we design for cost-efficiency from day one.
Where agentic systems beat simple chatbots
- Deep research: agents that gather from multiple sources, cross-check, and synthesize — not just answer from one document.
- Operations automation: multi-step back-office work: reconcile, investigate exceptions, draft, escalate.
- Complex triage: support and sales qualification that needs several tools and judgment calls per case.
- Continuous monitoring: agents that watch systems, investigate anomalies, and act within bounds.
If you need a conversational front end rather than an autonomous system, see our AI chatbot development service — and if you’re looking to hire an AI agent development company for ongoing builds, that’s a separate engagement model.
Our safety architecture for autonomous systems
- Least privilege: agents get the minimum tool access the task requires — nothing more.
- Irreversible actions need humans: spending, deleting, publishing, and external sends always hit an approval gate.
- Bounded autonomy: explicit limits on steps, cost per run, and escalation triggers.
- Full auditability: every action traceable to its reasoning and the data it used.
Why MTD for agentic AI
- Systems thinking, not demos: 9+ years building production software teaches you that the demo is 10% — reliability is the other 90%.
- Evaluation-driven: we don’t ship agents we can’t test. Every system gets a regression suite.
- Full-stack delivery: the tools your agents need — APIs, workflows, data pipelines — built by the same team.
- Honest about limits: we’ll tell you when a simpler automation beats an agent — and charge you for the simpler thing.
More questions, answered
How much does agentic AI development cost?
A focused single-domain agent typically costs $4,000–$10,000 fixed. Multi-agent systems with complex tool use run $10,000–$30,000+. Fixed quotes after a free scoping call.
What’s the difference between an AI agent and agentic AI?
An AI agent is a single assistant that acts toward a goal. Agentic AI is the broader engineering discipline: multi-step systems where agents plan, use tools, verify results, and coordinate — with the safety architecture that makes autonomy trustworthy.
How do you prevent agents from going rogue?
Least-privilege tool access, allowlists, spending and step limits, human approval on irreversible actions, and full audit trails. Autonomy is always bounded and observable.
How long does it take to build an agentic system?
A focused agent ships in 4–8 weeks including evaluation. Multi-agent systems take 10–16 weeks. The evaluation harness — proving the agent behaves — is a significant part of the timeline, and rightly so.
What models and frameworks do you build on?
Current frontier models (OpenAI, Claude) via API, with framework choices matched to the task — we avoid lock-in to any single agent framework and design for model portability.
When is agentic AI overkill?
When a deterministic workflow or a simple chatbot solves it. If the task has clear steps and no judgment calls, automation is cheaper and more reliable. We say this in scoping — before you pay for an agent you don’t need.
Have a multi-step problem that needs real autonomy? Talk to us — we’ll scope the system and its safety architecture before you commit.