Custom AI Agents — not demos. Deployed systems.
Production-ready agents built on Anthropic or OpenAI SDKs. Tool use, multi-step reasoning, long-running tasks. The kind of AI that actually runs your workflows, not impresses a demo audience.
What you get
Production AI agent with defined tool set, memory architecture, and task execution loop
Tool integrations — APIs, databases, web access, file systems, or custom tools as needed
Safety and guardrail layer — scope constraints, output validation, human-in-the-loop checkpoints where appropriate
Monitoring and observability — agent action logging, cost tracking, failure alerting
Testing suite covering tool use, multi-step tasks, and failure modes
Full documentation: agent capabilities, tool specs, deployment guide, extension guide
30 days post-delivery support
How it works
Agent scoping
Define the agent's scope: what tasks it executes, what tools it needs, what human oversight looks like, where it can act autonomously and where it needs approval. This is the most important step — an under-scoped agent is dangerous; an over-scoped one never ships.
Architecture design
Design the agent architecture: which model (Claude vs. GPT-4 vs. other), memory approach (in-context vs. external store), tool design, orchestration pattern. Written spec approved before build.
Core build
Build the core agent loop, tool integrations, and memory system. Demonstrated to you at week 2 or 3 — you see it running against real tasks before the system is complete.
Safety + testing
Add guardrails, output validation, and human-in-the-loop checkpoints. Test against real task scenarios including adversarial inputs and failure modes.
Deploy + monitor
Production deployment. Monitoring setup. Walkthrough session showing how to add tasks, review logs, and extend the tool set. 30-day support begins.
Tech stack
FAQs
What is an AI agent and when do you actually need one?
An AI agent is an LLM that can take actions — call APIs, read files, run code, search the web — not just generate text. You need one when the task requires multiple steps with real-world tool use, not just text generation. If a human executes a multi-step process using several different tools, an agent can often do the same.
How do you keep agents from doing things they shouldn't?
Scope constraints baked into system prompts and architecture. Output validation before actions execute. Human approval checkpoints for high-stakes actions. Action logging so you can audit what the agent did. A well-designed agent knows what it can't do, not just what it can.
Anthropic or OpenAI — which is better?
Depends on the use case. Claude (Anthropic) is generally better for long-context tasks, following complex instructions, and tasks requiring nuanced judgment. GPT-4 has broader fine-tuning options and some specific tool integrations. I'll recommend based on your specific agent design — not a blanket rule.
Can the agent access our internal systems?
Yes — tool access is scoped per your security requirements. The agent can be given read-only access, write access to specific systems, or any combination. We design the security model during the architecture phase.
What's the difference between an agent and an automation?
Automations follow a fixed script — if X then Y. Agents reason about what to do based on context. If your workflow has variable paths, exceptions, and judgment calls, you need an agent. If it's a consistent, predictable process, automation is usually simpler and more reliable.