AI Agent Creation
Purpose-built AI agents that handle real work, customer support, lead qualification, internal operations, and process automation built around how your team operates.
Most AI projects stall at the prototype.
Demos run, but edge cases break them, integrations are thin, and success was never defined before the build. A demo runs once. A production agent runs every day under real conditions, with real data, against real users.
- Use case definition and scoping
- Agent architecture and integration
- Prompt engineering and guardrail design
- Knowledge base setup and connection
- Build, testing, and deployment
- Performance monitoring and iteration
Agents we build.
- Customer support and triage agents
- Lead qualification and intake agents
- Internal knowledge and research agents
- Document processing and review agents
- Outbound and follow-up automation
- Multi-step workflow agents spanning tools
See if AI Agent Creation is the right move for your team.
Request a free quoteWe Create Agents and Keep Them Running
We create AI agents tailored to a specific job in your business, then manage and run them so they keep delivering. Our senior team builds with CrewAI, LangGraph, and n8n on top of Claude, GPT, and open models, and connects each agent to the exact systems and data it needs. From first build to live operation, we own the work rather than handing you a prototype to figure out alone.
We keep each agent under active monitoring after launch and update its behavior as your workflows evolve, so it stays useful instead of going stale.
- We scope each agent to a real task, then build it against your tools, data, and rules
- We use CrewAI, LangGraph, and n8n with the right model for the job
- We add guardrails, testing, and fallbacks so agents behave predictably in production
- We operate and update agents as your processes and inputs change
Production, not pilot.
We move past the pilot phase to production-ready systems, scenario tested, monitored for drift, and optimized on real performance after deployment.
Questions, answered.
We start with a discovery phase that maps the exact task, the systems the agent must touch, and what a successful handoff looks like. A focused single-purpose agent (for example, a lead-qualification agent that scores inbound form fills and books meetings) typically ships in 3 to 6 weeks, while multi-step ops agents that span several tools take longer. We scope each build with clear milestones so you know what ships first and what comes in later phases.
We build agents that integrate with the platforms you already run, including CRMs like Salesforce and HubSpot, help desks, Slack, email, databases, and internal APIs. For example, a support agent can read order history from your database, draft a reply in your help desk, and escalate to a human when confidence is low. If a system has an API or webhook, we can almost always connect to it, and we confirm every integration during scoping so there are no surprises.
We are model-agnostic and pick based on the job. Our stack includes Claude, OpenAI GPT models, and open models like Llama, Hermes, and Mistral, orchestrated with platforms and frameworks such as Salesforce Agentforce, LangGraph, CrewAI, and n8n. For sensitive or high-volume internal tasks we can run open models to control cost and data exposure, and because we build on standard frameworks you are not locked into a single vendor.
We constrain agents with grounded data sources, explicit tool permissions, and guardrails so they act only within defined boundaries, and we build in human-in-the-loop checkpoints for anything sensitive like refunds or outbound commitments. Before launch we test against real scenarios and edge cases, and we instrument the agent with logging so every action is traceable. We do not promise perfection, but we design for safe failure where the agent escalates to a person instead of guessing.
You own the agent, the prompts, the configuration, and the integration code we build for you. NYFTY does not just hand off and disappear, we can manage and run the agent on an ongoing basis, monitoring performance, tuning behavior as your data and processes change, and adjusting guardrails as models update. We also offer a lighter handoff option if your team prefers to operate it in-house, and we document everything either way.