Custom AI Agent Development —
Built for Production, Not Demos
We design and build AI agents that work in real systems — not sandboxes. That means tool use connected to your actual stack, persistent memory across sessions, multi-step reasoning that handles edge cases, and production-grade infrastructure with error handling, observability and fallbacks baked in from day one.
Services
What we build
Four categories of AI agent development, each requiring different architecture and production considerations.
Task Agents
Autonomous agents that execute complex workflows end-to-end. Connected to your APIs, databases and internal tools. They plan, act, verify and retry — without hand-holding.
RAG Agents
Retrieval-augmented systems that reason over your private documents, knowledge bases and live data. Beyond simple search — they synthesize, compare and cite with precision.
Multi-Agent Systems
Coordinated networks where specialized agents collaborate — one plans, one executes, one verifies. Designed for tasks too complex or too parallel for a single agent to handle.
Voice & Conversational Agents
Real-time voice agents with low latency, interruption handling and tool calls mid-conversation. Conversational AI that goes beyond scripted flows into genuine reasoning.
The gap
Why production-grade matters
The difference isn't the model — it's the engineering around it. Most pilots collapse the moment they hit real data, real users, and real failure modes.
Three reasons most agent projects never leave the demo stage:
No error handling
Agents that fail silently, loop infinitely or return hallucinated results when APIs timeout or inputs are malformed. In demos, inputs are always clean. In production, they never are.
No observability
You can't debug what you can't see. Agents without traces, logs and evals are black boxes. When something goes wrong — and it will — you have no way to know where, why, or how often.
No real integration
Demo agents query a mock API with 10 documents. Production agents call live systems with auth, rate limits, schema changes and permission scopes. The integration is 80% of the work.
How we work
Our process
Four phases from first call to deployed system. No month-long discovery phases. No slides-only deliverables.
Scope
We map the task, the failure modes, the data sources and the success criteria. We tell you what's feasible, what's overengineered, and what to skip entirely. Output: a concrete spec, not a proposal deck.
Architecture
We design the agent loop, memory strategy, tool contracts and fallback logic before writing a line of code. We choose the right framework for your stack — not the trendiest one.
Build & Test
We build in iterations with evals from day one. Every capability gets a test suite. We harden against real failure modes — not just happy-path flows. You see progress weekly.
Deploy & Handoff
We deploy to your infrastructure, set up monitoring and document everything your team needs to maintain it. We don't disappear at launch — we stay until it's stable.
FAQ
Common questions
Straight answers on timeline, cost, and what custom AI agent development actually involves.
How long does it take to build an AI agent?
A focused single-purpose task agent — well-scoped, connecting to one or two APIs — typically takes 3 to 6 weeks from spec to production deployment.
More complex systems take longer:
- RAG agents with large document corpora: 4–8 weeks
- Multi-agent pipelines: 6–12 weeks
- Voice agents with real-time constraints: 5–10 weeks
The biggest variable is integration complexity — how many systems the agent needs to connect to and how well-documented they are. Vague requirements are the leading cause of delays. We scope tightly before we build.
How much does custom AI agent development cost?
Pricing depends on scope, not on a rate card. That said, most projects fall into these ranges:
- Focused task agent: $5,000–$15,000
- RAG agent with knowledge base setup: $10,000–$25,000
- Multi-agent system: $20,000–$60,000+
- Voice agent (real-time): $15,000–$35,000
We also offer retainer arrangements for ongoing agent maintenance, monitoring and iteration — useful once a system is live and you want continuous improvement without a new SOW each time.
What's the difference between an AI agent and a chatbot?
A chatbot follows a predefined script or generates responses based on conversation history. It answers. An AI agent takes actions.
Agents have tools — they can call APIs, read from databases, write to systems, execute code, browse documents and chain multiple operations together. They reason about what to do next rather than just what to say next. They handle multi-step tasks autonomously: plan, act, evaluate the result, and adapt.
The line gets blurry with advanced conversational agents, but the core distinction is: chatbots produce text, agents produce outcomes.
Can you build agents for my existing software stack?
Yes — integrating with existing systems is the majority of what we do. We work with:
- REST and GraphQL APIs (authenticated, rate-limited, paginated)
- Databases (PostgreSQL, MySQL, MongoDB, vector DBs)
- SaaS tools (Salesforce, HubSpot, Notion, Slack, Google Workspace)
- Internal APIs and legacy systems with documentation
- Cloud infrastructure (AWS, GCP, Azure)
We don't require you to change your stack. We build the agent to fit your environment — not the other way around.
Do you use LangChain, LlamaIndex, or custom frameworks?
We use whatever is right for the project — not whatever is popular this month. Our current default toolkit includes LangGraph for stateful agent workflows, direct Anthropic and OpenAI SDK calls when frameworks add more complexity than they remove, and custom orchestration code for systems with tight latency or reliability requirements.
LangChain and LlamaIndex are good tools for the right problems. They're also frequently overused — adding abstraction layers that make debugging harder without adding real capability. We'll recommend them when they genuinely fit. We'll tell you when they don't.
Every project gets the simplest architecture that reliably solves the problem. We document all decisions so your team can maintain it independently.
What happens when the agent makes a mistake?
Agents make mistakes. Production systems are designed with that assumption built in. How we handle it:
- Structured evals: before deployment, we measure failure rates against a representative test set. You know the error rate before it goes live.
- Graceful degradation: when an agent hits an edge case it can't resolve, it hands off cleanly — to a human, to a fallback system, or with an explicit explanation of what went wrong.
- Observability first: every action is logged with enough context to reproduce and debug. We trace reasoning steps, tool calls and outputs.
- Human-in-the-loop checkpoints: for high-stakes actions, we build in approval gates before the agent commits changes to real systems.
The goal isn't a zero-mistake agent — that doesn't exist. The goal is a system that fails safely, fails visibly, and improves over time.
Ready to build something that actually ships?
Tell us what you're trying to automate. We'll tell you what's realistic, what it'll cost, and how long it'll take — in the first conversation.
No sales process. No NDAs required to talk.