The bespoke, heavy-build track: AI agents engineered from your workflow up; deep system integrations, custom guardrails, and evaluation built specifically for how your team works.
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.
AI Agent Development is the practice of engineering production AI agents from a team's actual workflow up: software that plans multi-step tasks, calls your real tools, and acts under defined controls, built with deep system integrations, custom guardrails, and evaluation specific to how your business operates.
Engineers map a real workflow, then build an agent with orchestrated control flow, memory, and tool access (using frameworks like LangGraph or CrewAI on top of models such as Claude or GPT), wired into your live systems with permissions, logging, and human approval gates. The agent is instrumented with tracing and task-specific evals so its behavior can be measured, corrected, and kept reliable in production.
For enterprise and mid-market teams whose core operations are too complex, high-stakes, or deeply integrated for an off-the-shelf tool; the outcome is throughput and reliability on a workflow that used to require heavy manual effort, with fewer errors and controls the team can actually trust.
A support operations team needs an agent that reads an incoming ticket, checks the customer record and order status across the CRM and billing system, drafts a resolution, and routes anything involving a refund to a human for approval, with every step logged and evaluated against real past tickets before it ever touches a live case.
See if AI Agent Development is the right move for your team.
Request a free quoteWe 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, Qwen, 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.
Illustrative example, styled to show the kind of output we deliver.
Agents and automations we’ve built to capture demand and take work off people’s plates.
Result Captured after-hours bookings that previously went unanswered and freed staff for live calls.
Result Cut routine turnaround time and shifted the team to higher-value work.
Examples are anonymized to honor client NDAs and edited to illustrate typical scope, outcomes vary by market, budget, and starting point.
Reliability comes from treating the agent as a bounded control loop with typed tools and a scored eval set, not a clever prompt. We build the failure containment and the measurement rig first, so behavior can be observed, regression-tested, and corrected instead of hoped for.
A single prompt can look flawless on a demo and still drift the moment inputs, models, or business rules change, because nothing is holding it to a standard. Wrapping the model in a bounded loop with typed, idempotent tools and a scored eval set turns "seems to work" into a number you can regress against, so every prompt or model change is checked against real cases before it ships. That is what lets an agent stay trustworthy under live volume instead of degrading silently.
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, Qwen, 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.