Enterprise AI agents
Enterprise AI agents that your security team can actually sign off on
An agent that works in a demo is easy. One that respects who is allowed to see what, logs every action, asks a human before anything irreversible, and keeps working after the model provider ships an update is the hard part. That is the part we build.
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Priced per project and scoped after a short architecture audit.
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Quick answer
AY Automate builds enterprise AI agents with role-based access, audit logging, human approval steps, monitoring and ongoing maintenance, integrated with existing business systems. Typical timeline: six to ten weeks to a governed pilot, scoped after the audit call. Priced per project and scoped after a short architecture audit.
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What it is
What makes an agent enterprise-grade
Four things separate a production agent from a prototype. It runs with the caller's permissions, not a shared super-user. It records what it did and why. It stops and asks when an action cannot be undone. And someone gets alerted when it fails or drifts.
We design those four in from the first sprint. Bolting governance on afterwards means redoing the data access layer, which is the most expensive part of the build.
We work with mid-market and growth-stage companies that have real compliance needs. We do not claim capacity for programmes that need hundreds of engineers, and we say so on the first call.
Fit check
Is this the right fit?
Where this wins
Regulated or audited environments
Where every action needs a record and access is scoped by role, for example aerospace, healthcare and finance teams.
Internal agents over sensitive company data
Agents that answer questions and produce reports from your database while each person only sees what they are cleared for.
Customer-facing agents with a support cost problem
Agents that resolve most requests alone and hand the rest to a person with full context.
When NOT to use this
- You want a chatbot over public documents. A simpler build or a chatbot page fits better and costs less.
- Nobody in the company owns the agent's outcome. Ungoverned agents cause more trouble than the manual work they replace.
- You need a certified compliance programme delivered. We build to your controls, we do not issue certifications.
How it works
How an enterprise agent build runs
Narrow first. One agent, one job, one owner, then widen access as it earns trust.
- 01
Scope and risk review
Week 1Name the job, the owner, the data it touches and the actions that must never run unattended.
DeliverableOne-page agent charter with risk register
- 02
Access and tool design
Week 1 to 2Define per-role scopes, the tools the agent may call and the approval rules for each.
DeliverableAccess model and tool specification
- 03
Build and evaluate
Week 2 to 6Build against sandbox or sample data, with a test set of real questions and known-bad inputs.
DeliverableWorking agent with an evaluation suite
- 04
Pilot with a small group
Week 6 to 8A small group uses it on real work while we watch logs, failures and cost.
DeliverablePilot report and fixes
- 05
Operate
Monitoring, alerting, model and dependency updates, and monthly review of failures.
DeliverableRunbook and maintenance plan
Typical timeline
Typically six to ten weeks to a governed pilot, scoped after the audit call
Stack we build with
Claude · OpenAI · MCP · PostgreSQL · Supabase · TypeScript · Python · n8n
Use cases
Where enterprise agents earn their keep
Ask your own database
Non-technical staff query company data and get reports and charts limited to what they may access.
Multilingual support agent
Handles the bulk of inbound requests around the clock and escalates the rest with context.
Sales research agent
Prepares accounts, updates the CRM and scores leads before a call.
First 30 days
What you have after 30 days
The controls exist before the agent does anything important.
Week 1
Agent charter
The job, owner, data sources and actions that always require approval, written down and agreed.
Week 2
Access model
Who sees what, enforced in the data layer rather than in a prompt.
Week 3 to 4
Working agent on sandbox data
Running against sample data with logging on and an evaluation set in place.
Day 30
Pilot plan
Named pilot users, success measures and the alerting that will watch it.
Scope
Scope and exclusions
In scope
- Agent architecture, tool design and the access model
- Integrations with your existing systems and identity provider
- Approval steps, audit logging and evaluation tests
- Monitoring, alerting and a maintenance plan
Not in scope
- Security certifications or attestations for your company
- Training or hosting your own foundation model
- Replacing your identity or data platform
- Agents that act on money or safety-critical systems without a human step
Results
Results from comparable work
Achieved results only. Clients that have not agreed to be named are described instead.
The whole database became an agent anyone can talk to. Each person gets their own scope and only sees and generates reports and charts for what they are allowed to touch.
A multilingual WhatsApp AI agent now handles 80% of requests without a human touching them. Response time had averaged over 4 hours. The client reports roughly $18,000 a year saved and support workload down 40%.
Agents do pre-call research, update the CRM and score leads automatically. Research time per prospect dropped and the sales team's conversion rate went up.
Pricing
How this is priced
Priced per project and scoped after a short discovery call, not sold as a fixed package. Cost follows the number of systems, the access model and the security requirements.
Scoped build
Scoped after a discovery call
Most engagements start with one narrow, high-value piece so you see it running in production before anything expands.
Embedded engineer
From $3K/month
A dedicated engineer building and maintaining the work inside your team, instead of a scoped project.
Free 30-min call. No pitch, no slides.
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Find what to automate first, and how AI sees you
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Use Claude Code, Cursor or Codex? Paste this into your agent
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Describe the agent and the data it would touch
A 30 minute call. We look at the job, the data and the controls you need, and tell you what a governed first version would take.
Book a 30min Free Strategy Call
In this call, we'll walk through your project scope, timeline, and goals - so we can both check if we're a fit. No obligation, no slide deck, just a working session.
Don't want a call? Email walid@ayautomate.com
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COO, Adstronaut.io
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FAQ
Enterprise AI agent questions
What are enterprise AI agents?+
Enterprise AI agents are AI systems that carry out business tasks across company tools, such as a CRM, a database or a ticketing system, under the same access rules, logging and approvals as an employee. Unlike a chatbot, an agent takes actions, so it needs scoped permissions, an audit trail and a human sign-off on anything that cannot be undone.
How do you keep an agent from seeing data it should not?+
Access is enforced in the data layer, using the caller's identity and role, not in the prompt. A prompt can be talked around. A query the database refuses cannot.
Can the agent take actions, or only answer questions?+
Both, with a rule per action. Reversible, low-risk actions can run unattended. Anything irreversible, or involving money, waits for a person to approve.
Do you host the agent or do we?+
Either. Many teams keep it in their own cloud account so data stays inside their boundary. We design to whichever hosting model your security team approves.
What does it cost?+
Priced per project and scoped after a short architecture audit. Cost follows the number of systems, the access model and your security requirements. Most engagements begin with one narrow agent so you see measured results before expanding.
Who maintains it after launch?+
We can, under a maintenance plan covering monitoring, model updates and monthly failure review. Or we hand it to your engineers with a runbook.