Autonomous agents that reason, plan, and act.
We design enterprise AI agents that handle multi step work end to end, pulling data from your systems, making decisions against your rules, and executing actions across CRMs, ERPs, ticketing tools, and internal APIs. No human in the loop required.
Built for real work,
not demos.
Our agents don't just answer prompts, they reach into your systems, reason over real data, and execute concrete actions. Every capability below ships with monitoring, guardrails, and a human in the loop fallback you can switch on.
Multi Step Reasoning
Agents plan a sequence of actions, evaluate intermediate results, and self correct when something doesn't match expectations.
Tool & API Use
Native connectors for Salesforce, HubSpot, Slack, Jira, SAP, Zendesk, Postgres, and any REST/GraphQL endpoint you expose.
Long Running Memory
Persistent context across sessions, accounts, and threads so the agent picks up exactly where the customer or workflow left off.
Guardrails & Policy
Approval gates, write action allowlists, and audit trails for every decision, so legal, security, and ops sign off without friction.
Multi Agent Orchestration
Specialist agents that coordinate, one researches, another drafts, a third executes, with a planner managing the handoff.
Observability Built In
Trace every reasoning step, tool call, and token spend. Drift detection and replay tooling come standard, not as add ons.
The workflows companies actually hand to agents.
The AI projects that survive their first budget review share one trait: they started with a workflow, not a technology. These are the six places we deploy agents most, and what the work looks like when it lands.
Customer Support Agents That Close Tickets
Not deflection bots that frustrate customers into email. Agents that read the ticket, pull the account history, check the order system, issue the refund or the fix, and write back with the resolution, escalating to a human only when the case genuinely needs one. For the conversational front end on sales and service, this pairs with our sales and CRM chatbot work; this page is about the agent that finishes the job behind it.
Back Office Agents for Document Heavy Work
Invoices matched to purchase orders, claims triaged against policy rules, contracts checked for missing clauses, onboarding packets processed end to end. This is where the research says AI actually pays: MIT's study of enterprise AI found the rare successes concentrated in exactly this kind of specific, workflow integrated back office automation. The document understanding layer comes from our document AI practice; the agent is what acts on it.
Sales and Revenue Operations Agents
Leads are researched, scored, and routed within minutes of arrival. CRM records that update themselves after every call. Follow ups that go out on time because no human had to remember them. Revenue teams do not need more dashboards telling them what fell through the cracks; they need an agent standing in the crack.
Agents That Operate Your Own Systems
The highest value agents are rarely the ones talking to customers. They are the ones working your internal systems: reconciling data between platforms, processing orders across your ERP and logistics tools, running the checks a human runs today. This is where our agent ready API and MCP work matters most, because an agent is only as capable and as safe as the interfaces it acts through.
Agents for Regulated Industries
Finance, healthcare, insurance, and anyone touching European data get a different build: decision logging an auditor can replay, data flows designed for residency rules, and human approval gates where the regulation demands them. Our GDPR compliant AI practice handles the framework so the agent ships compliant instead of retrofitted.
Agents Grounded in Your Data
An agent reasoning over stale or contradictory data makes confident mistakes at machine speed, and bad data is the number one reason enterprise AI pilots die. Before any agent touches production, we make sure the data foundation underneath it is governed, current, and tested, because the model is never the weak point. The data is.
The 95 Percent Statistic Is Why Our Process Looks Like This.
MIT found that 95 percent of enterprise AI pilots deliver zero measurable return. The same study found the successes share a pattern: narrow scope, deep workflow integration, and specialized external partners, roughly double the success rate of generic internal builds. That pattern is our process. Workflow mapping before models, shadow mode against real traffic before go live, and accuracy measured before anyone celebrates. We did not design our method to sound rigorous. We designed it to be in the 5 percent.
An Agent Is an Identity, and Identities Need Rules.
Every agent we deploy gets what a new employee gets: scoped permissions, credentials that expire, and an audit trail of everything it touched. It can create the invoice and never delete the customer table. This discipline comes from our zero trust identity practice, and it is the reason our clients' security teams sign off instead of slowing down.
From idea to deployed agent.
Four stages, no surprises. Most engagements ship a working agent in production within six to eight weeks.
Workflow Mapping
We sit with the team that does the work today and map every decision, system touchpoint, and edge case the agent will need to handle.
Architecture & Tools
We pick the right model, scope tool access, and design the prompt and memory architecture, delivered as a clear technical spec.
Sandboxed Pilot
The agent runs in shadow mode against real traffic for two weeks. We measure accuracy, escalation rates, and cost per task before going live.
Production & Iterate
Phased rollout with kill switches, monitoring, and biweekly tuning. We hand over runbooks and stay on for ongoing support.
Agent economics, the failure statistic, and the build or buy call.
AI agents are the most hyped and most doubted purchase in enterprise software right now, and both crowds are working from real numbers. Here are those numbers, in the open, before any sales call.
A focused single workflow agent typically runs $15,000 to $75,000. Most mid market deployments land between $25,000 and $120,000, and multi agent systems coordinating several specialists run $100,000 to $500,000 and up. The surprise for most buyers: the model is the cheap part. Integration with your real systems and the safety testing that keeps the agent on policy together consume 40 to 60 percent of the build. That is also why our quotes follow workflow mapping, not the other way around.
The number vendors put on slide two, and we put on slide one: the build is only 25 to 35 percent of what you will spend over three years. A production agent serving real volume typically runs $3,000 to $13,000 a month across model usage, infrastructure, monitoring, and tuning, and annual maintenance runs 15 to 30 percent of the build cost. Every proposal we write includes a projected cost per task and a three year total, and our AI cost optimization practice exists because that per task number can usually be cut hard once real usage data comes in.
MIT's research found 95 percent of enterprise AI pilots produce zero measurable P&L impact, and Gartner expects over 40 percent of agentic projects to be cancelled by 2027. The causes are consistent and almost never the model: data that was not ready, no success metric defined before the build, generic tools bolted onto specific workflows, and nobody owning adoption. The 5 percent that succeed are narrow, deeply integrated, and built with specialist partners. Our answer is structural: we define the success metric in workflow mapping, run the agent in shadow mode against real traffic for two weeks, and measure accuracy, escalation rate, and cost per task before go live. If the pilot numbers do not clear the bar, we tell you, and you have spent a fraction of a failed rollout finding out.
Sometimes the platform is the right answer, and we will say so. Platform agents win when the workflow is a commodity, lives inside one ecosystem you already pay for, and needs no unusual logic: a Salesforce shop automating Salesforce tasks should look hard at Agentforce first. Custom wins when the workflow crosses systems, encodes rules that are actually your competitive edge, faces compliance requirements the platform cannot evidence, or when per conversation platform pricing turns ugly at your volume. The honest math is a three year cost comparison at your real usage, and we build that comparison into every scoping engagement, both options priced.
For well scoped agents on high volume repetitive work, payback in 4 to 8 months is genuinely achievable, and industry surveys show roughly three quarters of mature AI deployments meeting or beating their ROI targets. The caveat that keeps those numbers honest: ROI only exists where a baseline was measured first. Before any build, we document what the workflow costs today in hours, errors, and delay, so the after picture is arithmetic instead of a vendor's slide. Complex multi agent transformations run on longer horizons, and anyone promising them a six month payback is selling you the 95 percent.
Not your hardest problem. Your most boring one. The ideal first agent workflow is high volume, repetitive, rule describable, backed by data that already exists, and tolerant of a human approval gate while trust builds. Invoice matching beats strategic forecasting. Ticket triage beats customer retention. The goal of the first agent is not transformation, it is a working system with provable numbers that earns the second and third agent their budget. Companies that start with the moonshot usually end up in the cancellation statistic; companies that start boring end up with a portfolio.
Bring us the workflow your team complains about most. In one call we will tell you what an agent would cost, what it would save, and whether the honest answer is that you do not need one yet.
Questions about
Enterprise AI Agents
A chatbot replies. An agent acts. Our enterprise agents plan multi step work, call your APIs, write to your databases, and verify the result, closing the loop on a task instead of just answering a question about it.
You'll see a working prototype inside two weeks. Most teams reach a sandbox pilot in four to six, and full production rollout in six to ten, depending on integration complexity and approval gates on your side.
Every agent ships with a policy layer: action allowlists, dollar value approval gates, rate limits, and a human in the loop fallback you can flip on per task. Plus full audit trails for every decision the agent makes.
Whatever fits the job. We work across Anthropic, OpenAI, Google, and open source models, and we're routinely model agnostic in the architecture, so you can swap providers later without a rewrite.
Yes. We deploy into AWS, GCP, Azure, and air gapped on prem environments. For regulated workloads, see our on premise AI offering.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. We'll map your use case, scope a working prototype, and tell you honestly whether AI is the right tool for the job.
info@croncore.com