AI Agent Development
Custom AI agents built around your actual workflows. Not demos, not generic tools, production-ready systems designed for how your team works.
From workflow to working system.
An agent is only as useful as the workflow it sits inside. Before anything gets built, we map the process, inputs, outputs, decision points, edge cases, and escalation logic.
- Requirements gathering and workflow mapping
- Agent design, prompting, and guardrail development
- Knowledge base and system integration
- Build, testing, and deployment
- Monitoring, iteration, and ongoing support
Who we build for.
- Off-the-shelf AI tooling is too generic to trust
- You need purpose-built solutions with guardrails and evaluation
- Previous AI implementations failed due to undefined processes
See if AI Agent Development is the right move for your team.
Request a free quoteEnterprise AI Agents We Develop and Operate
We develop production AI agents for complex enterprise and mid-market operations, then we run them. Our senior engineers build on LangGraph, CrewAI, and n8n, integrated with Salesforce Agentforce and powered by models such as Claude, GPT, or open models like Llama, Hermes, and Mistral. These are agents wired into your real tools and data with the controls enterprise teams require, not demos that fall over outside a sandbox.
We instrument every agent with tracing and evaluation in production, to help detect drift and failures early, reduce customer impact, and support faster remediation.
- We architect multi-step agents with tools, memory, and clear control flow using LangGraph and CrewAI
- We integrate agents into your live systems with permissions, logging, and human approval gates
- We pick and combine models per task, from Claude and Agentforce to GPT and open models
- We deploy, monitor, and maintain agents in production, owning reliability and cost over time
Reliability by design.
We define success criteria before development starts. Every agent goes through real-scenario testing before deployment, with monitoring for edge cases, drift, and iteration based on actual performance.
Questions, answered.
Most agents go from kickoff to a working pilot in 3 to 6 weeks, with full production hardening typically landing in 6 to 10 weeks depending on how many systems it touches. We start with a tightly scoped first version that handles your highest-value workflow, then expand once it is proven in real use. Complex multi-step agents that write back to systems of record sit at the longer end of that range because of testing and guardrails.
We build agents around your actual stack rather than a fixed list of connectors. Common integrations include Salesforce and Agentforce, your CRM, helpdesk and ticketing tools, internal databases, email and calendars, Slack, and custom APIs, wired together with frameworks like LangGraph, CrewAI, or n8n. For example, a support agent can read a ticket, look up the account in Salesforce, draft a reply in your voice, and log the resolution back to the case without a human re-keying anything. If a system has an API or database, it can almost always be connected.
You own it. We hand over the source code, prompts, configuration, and documentation, and we deploy to your infrastructure or cloud accounts so the agent and its data stay under your control. We also avoid lock-in by building on open frameworks and supporting model choice, including Claude, GPT, and open models like Llama, Hermes, and Mistral, to reduce vendor lock-in and preserve model portability where feasible, rather than leaving you with a black box you cannot inspect.
We design guardrails into the agent from day one rather than bolting them on later. That includes scoped permissions, human-in-the-loop approval for high-stakes actions, validation checks on outputs, logging of every step, and fallback behavior when the agent is uncertain. For example, a finance agent might draft and queue an invoice adjustment but require a person to approve anything above a set dollar threshold. We tune that balance of autonomy versus oversight with you based on your real risk tolerance.
Both. Many clients have us build the agent and then manage it on an ongoing basis, since models, prompts, and your own workflows all change over time. Managed support covers monitoring, performance tuning, prompt and model updates, adding new capabilities, and handling edge cases that surface once the agent is live with real volume. If you have an internal team that wants to take it over, we hand off cleanly with documentation and training instead, so ongoing management is your choice, not a requirement.