AI agent development services
We build agents that plan, decide, and act. We build agentic AI solutions that plan and act on their own. At COAX Software, we've automated logistics platforms for fifteen years. That experience shows us exactly where agents break in production.
Industry leaders we work with
Why agentic engineering?
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Types of AI agents we build
Travel booking agents
Booking flows break the moment inventory, pricing, and availability drift out of sync. Our AI agent development experts build tools that check real-time rates, hold inventory, and confirm bookings across providers. The agent doesn't just recommend a slot. It books it, confirms it, and flags exceptions for review.
Supply chain agents
A late shipment costs more the longer it stays hidden. We build agents that watch inventory levels, carrier data, and delivery timelines together. When a threshold breaks, the agent reroutes, reorders, or alerts a planner directly. On DriveIQ's dispatch layer, for instance, automated clustering cut diagnosis time from twelve minutes to under three.
Customer support agents
Most support tickets follow patterns a human doesn't need to solve twice. We build agents that resolve routine requests, pull account context, and escalate only what actually needs a person. Agentic AI integration with your existing helpdesk means the agent works inside tools your team already uses. No new inbox for anyone to check.
Sales agents
Pipeline data means nothing if reps spend hours qualifying leads by hand. We build agents that score leads, draft outreach, and update your CRM automatically. The agent flags high-intent prospects before a rep even opens the account. That's the difference between a dashboard and a system that moves deals forward.
Operations agents
Daily operations run on dozens of small decisions nobody has time to review. We build agents that monitor workflows, catch anomalies, and trigger the next step automatically. On SunsetLimo, we replaced six hours of manual reconciliation with a system that runs the same job in four minutes. That's custom AI agent development applied to a real bottleneck.
HR agents
Hiring and onboarding stall out on repetitive admin work. We build agents that screen resumes, schedule interviews, and route onboarding tasks to the right person. Recruiters keep the judgment calls. The agent handles everything that doesn't need one.
Finance agents
Commission errors and reconciliation gaps compound fast when nobody's watching closely. We build agents that validate records, apply business rules, and flag exceptions before they reach your books. Agents do what a skilled finance agent should do: enforce rules consistently, every single time.
Compliance agents
Regulatory paperwork rarely gets easier as a company grows. We build agents that track documentation, flag missing records, and generate audit trails automatically. Every action stays logged and traceable. That matters most when a regulator or partner asks you to prove it.
Technologies we work with
LangChain
LangGraph
OpenAI
Anthropic Claude
AWS Bedrock
AutoGen
Azure
Python
CrewAI
Pinecone
Zapier
n8n
AWS
GCP
TypeScript
Why our clients trust us
Our agentic AI development roadmap
We start by mapping your actual workflows, not pitching a generic agent. That means interviews with your team, a look at your data, and a hard read on where automation pays off first. You get a validated use case before anyone writes code.
Deliverables: process audit, use-case scoring, and a scoped engagement plan.
Every agent replaces a decision someone makes today. We document that decision step by step: inputs, rules, exceptions, and who signs off. Skip this step, and the agent inherits your team's blind spots instead of fixing them.
Deliverables: process maps, decision logic documentation, and edge-case inventory.
This is where true agentic software engineering is going on. We design how the agent plans, calls tools, and hands off to a human. Permission boundaries, memory structure, and failure handling all get mapped before development starts.
Deliverables: architecture diagram, tool integration plan, and a governance model.
We build a working agentic AI framework scoped to one narrow task, not the full vision at once. You test it against real data early, not a polished demo months later. Rule-based logic often runs first, while models tune in the background.
Deliverables: functional prototype, test results, and a go/no-go recommendation.
Once the prototype proves out, we build the full system. That means monitoring, logging, error handling, and integration with your existing tools. Every agent ships with an audit trail from day one.
Deliverables: production environment, integration pipelines, and monitoring dashboards.
Agents drift as your data and rules change. We stay attached after launch, tuning prompts, adjusting logic, and catching failures before they compound. Working with the right agentic AI providers and models matters here. We wrap what performs best, not what's trendiest.
Deliverables: quarterly performance review and an updated optimization roadmap.
Trusted by our clients
Dan Brooks
President, Krytter
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Bohdan Zabawskyj
Co-founder and CPO at Fortay.ai
Neil Winkworth
ex-CTPO, Location Live
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Joseph Heenan
CEO, Proteineer
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Mykola Bronitskyy
Co-founder, GrandBus
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See why clients recommend us
Our related services
FAQ
Cost depends on scope, not headcount. A discovery and architecture phase usually runs a few weeks and gives you fixed pricing before any build starts. We break agent builds into phases so you approve spend before committing further. Multi-agent systems and legacy integrations push costs up fast. That phased structure protects your budget from scope creep entirely.
Testing runs differently than standard QA. We validate decision logic against edge cases before an agent ever touches live data, then run it in shadow mode alongside your existing process. Dispatchers or reps review agent recommendations without acting on them first. Only once accuracy holds steady do we let the agent act autonomously on real transactions.
Every agent we build includes a fallback path for exactly this. High-stakes actions route through human approval by default until confidence scores prove reliable. When an agent does misfire, audit logs trace the decision back to its trigger within minutes. That traceability is why our DriveIQ platform caught fatigue risks before violations occurred, not after damage was done.
Ask every vendor for a phased scope with pricing per stage, not one lump sum. Many quote a single fee that hides scope creep once discovery reveals real complexity. Look for a team that shows past integration work, not just AI buzzwords. Ask what happens if a model underperforms, and whether human review sits inside the workflow.
You do, fully. Every contract with us transfers complete IP ownership at delivery, including source code, prompts, and documentation. There's no vendor lock-in and no licensing dependency on our infrastructure once the project closes. If you switch providers later, you walk away with everything you need to keep the agent running, no negotiation required at any point.
What we’ll do next?
1
Contact you within 24 hours
2
Clarify your expectations, business objectives, and project requirements
3
Develop and accept a proposal
4
After that, we can start our partnership
