Customer-support agents
Handle the long tail of tickets end-to-end — look up orders, issue refunds, update accounts, escalate edge cases to humans with full context.
AI LAB · 02
Autonomous workflow systems
AI agents that take action — not just answer questions.
A chatbot tells you about the refund policy. An agent processes the refund. We build production-grade AI agents that plan multi-step tasks, call your tools and APIs, handle edge cases, and ship the work — with scoped permissions and audit trails so you stay in control.
What we build
Agents that do real work.
The most common patterns we ship for clients — each one replacing hours of manual ops every week, or unlocking work that used to be too expensive to attempt.
Research agents
Multi-step research across web, internal docs, and databases. Produces sourced briefs in minutes, not days.
Sales & BDR agents
Qualify inbound leads, enrich data, draft personalised outreach, and book meetings — handing warm conversations to your reps.
Operations agents
Process invoices, reconcile accounts, generate reports, and trigger workflows in your ERP, accounting, or HRIS.
Code & QA agents
Triage bug reports, propose fixes, generate test cases, and run regression suites — integrated with your CI pipeline.
Multi-agent systems
Specialist agents that delegate to each other — a planner agent breaks down the task, executors do the work, a reviewer validates.
Use cases
Where agents pay for themselves.
Three deployments where the math worked out faster than anyone expected.
Tier-1 support agent
An agent connected to Zendesk, Stripe, and the product database. Handles password resets, refund requests, plan changes, and account merges — escalating only the genuinely complex cases.
82%auto-resolved
−63%response time
Inbound lead enrichment
The moment a form is submitted, an agent enriches the lead from LinkedIn, Crunchbase, and the company's website — then drafts a personalised first email and books a slot in the AE's calendar.
4×response speed
+27%meeting rate
Invoice processing agent
Watches a shared inbox, extracts line items from PDF invoices, matches them to POs, flags discrepancies for review, and pushes approved ones into NetSuite. The finance team handles exceptions, not data entry.
15h/wksaved
99.4%extraction accuracy
The stack we use
Frameworks, not magic.
Building reliable agents is mostly engineering — orchestration, state, retries, observability. Here's what we reach for.
Agent frameworks
- LangGraph
- CrewAI
- AutoGen
- OpenAI Agents SDK
Tool integration
- Model Context Protocol
- Function calling APIs
- Zapier, n8n, Make
- Custom REST adapters
Memory & state
- Redis short-term
- Postgres long-term
- Pinecone semantic
- Mem0, Zep
Observability
- LangSmith
- Langfuse
- Helicone
- Custom dashboards
How we work
From workflow to production agent.
01
Workflow audit
We sit with the operator doing the work today. Map every decision, every tool touch, every edge case.
02
Tool design
Define the tools the agent will use — scoped permissions, dry-run modes, structured inputs and outputs.
03
Prototype
Two-week build of the agent against real cases. We use the same data the operator sees.
04
Guardrails
Approval steps, rollback paths, rate limits, hallucination checks, escalation logic.
05
Shadow mode
Agent runs alongside humans for two weeks. We compare every decision to the human baseline.
06
Production & tune
Roll out gradually. Monitor cost, accuracy, and edge cases. Iterate weekly.
Frequently asked
Common questions about agents.
What is an AI agent?
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An AI agent is a software system powered by a large language model that can plan multi-step tasks, call external tools and APIs, and take actions to achieve a defined goal. Unlike a simple chatbot, an agent reasons about which steps to take, executes them, observes the results, and adjusts.
What's the difference between a chatbot and an AI agent?
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A chatbot responds to messages, while an AI agent takes action. A chatbot might answer "How do I refund this order?". An agent looks up the order, checks the refund policy, processes the refund, and sends a confirmation email — all without further human input.
How long does it take to build a custom AI agent?
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A focused single-purpose agent (one workflow, two or three tools) ships in 4 to 6 weeks. Multi-agent systems with planning, memory, and review loops typically take 8 to 14 weeks. We always start with a 2-week prototype against real data.
How much does AI agent development cost?
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Custom AI agent projects at Appsmediaz typically range from $15,000 for a focused single-workflow agent to $80,000+ for multi-agent systems with planning, memory, evaluation, and human-in-the-loop review. We provide fixed quotes after a discovery sprint.
Can AI agents safely take real actions?
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Yes, with the right guardrails. We design agents with scoped tool permissions, dry-run modes, human approval steps for high-stakes actions, comprehensive audit logs, and automatic rollback for failed transactions. Critical actions like payments or deletions always require explicit confirmation.
Explore the rest of the AI Lab
Got a workflow that's screaming for an agent?
Book a free 30-minute call. We'll tell you honestly whether agents fit, what they'd cost, and where they'd break.
Schedule a call