AI agents · MCP · Controlled execution
DEIENAMI engineers AI agents that interact with approved tools, retrieve business information, and coordinate multistep work across existing applications — with defined permissions, human approval and operational visibility.
Start with one objective and one agent. Expand when value is demonstrated. This page is not a public price list.
Business problems
Three situations where people bridge systems by hand — and a controlled agent can prepare or complete the next step.
Operations
Interactive examples
Illustrative demos with sample data only. No live model or production connection.
IT service desk agent · illustrative
Flow
Status
What we build
Six practical paths. We start with one objective and one agent, not open-ended autonomy.
Agents that prepare or complete operational work across approved applications with an audit trail.
Expose internal tools and context through controlled MCP interfaces your agent can call.
Connect CRM, ERP, identity, docs and custom APIs with least-privilege access.
Coordinate tool calls, retries and budgets so multistep work stays observable.
Route consequential actions to a named owner before anything is written.
Execution logs, failure cases, intervention rate and a path to pause the agent.
How agentic systems work
MCP standardises how agents reach tools and context. It does not replace authorization, business rules or your application security model.
A person or system event starts the run. The agent receives a goal, not unrestricted rights.
Best when steps are predictable and rules are enough. See Workflow automation.
Best when AI prepares work inside a fixed flow. See AI workflow automation.
Best when the agent must choose tools and steps within policy to complete a goal.
Best when you need a shared, controlled interface for tools and context. See System & API integration.
Engineering proof
Professional agent engineering goes beyond a demo chat: permissions, approvals, evaluation and recovery are designed in.
Sample
Request, plan, tools, approval, action and verification on one diagram.
Sample
What tools exist, what they may read or write, and how results are validated.
Sample
Allow, deny or ask — least privilege for every consequential tool.
Sample
Named owners, reject paths and an audit record for sensitive actions.
Sample
Queues, budgets, failures and retries stay visible for the scoped agent.
Sample
Known failure cases, intervention rate and go / no-go notes for Prove.
How we engage
Scope boundaries for evaluation. Final terms follow qualification. Third-party software and model usage costs are separate.
Assess
Qualify the problem
Limited review for suitable enquiries. Not unpaid consulting.
Request an Agent Feasibility ReviewProve
Quoted after qualification
Fixed-scope demonstration of value before wider rollout.
Discuss a proof of valueDeploy
Scoped after Prove
Multi-agent programmes are scoped separately.
Discuss deploymentOperate
Separate agreement
No unlimited support. No unrestricted autonomy on this page.
Discuss careIf the task is predictable and rules or APIs are enough, prefer deterministic automation or a simpler integration. See Workflow automation, AI workflow automation and System and API integration.
Straight answers on MCP, permissions, mistakes, ownership and costs. The contract still sets the exact terms.
Model Context Protocol is an open way for AI applications to connect to external tools and data sources through a shared interface pattern.
No. Agents can use APIs, databases or other connectors. MCP helps when you want a consistent, controlled tool interface.
Usually when approved APIs, connectors or MCP servers are available. Compatibility and least privilege are confirmed in Assess.
Allow-listed tools, policy checks, human approval for high-risk steps, execution budgets and audit logs. MCP does not replace your authorization model.
We design retain, alert, retry and escalate paths. Consequential writes should not look successful when they did not happen.
Yes. Prove is built for one business objective and one agent before wider rollout.
Budgets, tool-call limits and evaluation in Prove. Third-party model and software costs remain separate and are confirmed during scoping.
Ownership, licensing and support terms are defined in the contract for the engagement.
Operate covers monitoring, incident path, evaluation refresh and bounded enhancements under a separate agreement. No unlimited support claim on this page.
Qualified enquiry
Tell us about a task that requires employees to work across multiple applications. We will review whether a controlled agent, conventional automation or an integration is the most appropriate approach. Your submission creates a New lead, not an automatic Qualified opportunity.
Your enquiry is logged as a New lead. A person will triage it. This is not yet a Qualified opportunity.
