Agentica Technologies

Agentic AI Enterprise transformation Security Governance

Building the
Agentic Enterprise.

We help organizations design, build, secure, govern and scale Agentic AI, AI Agents and AI-driven applications—from strategy and architecture to production deployment.

Start Your AI Transformation Explore Agentic AI
Enterprise Agentic AI Control plane
  • Identity
  • Agents
  • Models
  • Tools
  • Memory
  • Security
DesignStrategy and architecture BuildAgents and applications SecureAI and GenAI security GovernAI GRC and controls ScaleMLOps and AgentOps

Why agentic AI

From AI that answers to AI that acts.

Generative AI produces content. An agent pursues a goal: it reasons, plans, calls tools, reads enterprise knowledge, takes actions and works with other agents until the job is done.

That shift moves the hard problem from model quality to control. What the system is permitted to do, who approves it, and what evidence it leaves behind.

The autonomy ladder Five rising steps from rules-based software through generative AI, copilots, AI agents and multi-agent systems. A meter under each step shows that the control and oversight required rises with autonomy. Most enterprise AI today sits at generative AI and copilots; Agentica works on the final two steps. autonomy where Agentica designs, secures and governs where most enterprise AI sits today Rules-based software Generative AI Copilots AI agents Multi-agent systems deterministic, no judgement drafts and summarises assists a person in the loop plans, calls tools, takes actions agents delegate to other agents control and oversight required at each step
Autonomy and required control rise together. Most failed AI programmes try to climb the ladder without building the control surface underneath it.

What we do

One partner from strategy through to production.

Advisory, engineering, security, governance and operations across the whole AI lifecycle — with a named deliverable at the end of each engagement.

Agentic AI advisory

Opportunity discovery, readiness assessment, target architecture, sequencing and the business case that survives finance review.

You end withTarget architecture, prioritised roadmap, costed business case

AI transformation

Redesign of processes, operating model, platform choices and the internal capability needed to run AI rather than pilot it.

You end withOperating model, capability plan, adoption programme

Agent engineering

Production AI agents, multi-agent workflows and enterprise copilots built on your identity, your data and your systems of record.

You end withRunning agents, tool registry, approval workflows

AI applications

AI-native products and intelligent applications, integrated with ERP, CRM, document stores and the legacy estate that is not going away.

You end withDeployed application, integrations, handover documentation

AI engineering and MLOps

Evaluation harnesses, observability, model routing, cost control, release pipelines and the operational discipline that keeps quality stable.

You end withEval suite, traces and dashboards, release pipeline

AI security and governance

Threat modelling, red teaming, guardrails, agent permissions, policy enforcement and the evidence pack for regulators and auditors.

You end withThreat model, control set, audit evidence pack

Reference architecture

The model is not the application.

Enterprise value comes from the stack around the model: identity, tools, memory, workflow, policy, security and observability. That stack is what we design and build.

Review your architecture with us
Enterprise agentic AI reference architecture Five stacked layers — identity and access, agentic control plane, agent runtime, model and tool gateways, and knowledge, memory and state — with security, governance and observability running vertically across all of them. CROSS-CUTTING Security, governance and observability Guardrails and filtering Prompt and action audit log Evaluation and regression Cost and usage control SIEM and alerting Model and agent inventory EU AI Act evidence Human approval records Red team findings Incident response hooks Applies to every layer, every agent and every tool call — not bolted on after go-live. L1 Identity and access Entra ID · OAuth 2.0 / OIDC · RBAC and ABAC · agent identity · secrets L2 Agentic control plane Agent registry · tool registry · policy engine · human approval · budgets L3 Agent runtime Microsoft Agent Framework · LangGraph · OpenAI Agents SDK · custom L4a Model gateway Claude · GPT · Gemini · open models L4b Tool and MCP gateway APIs · ERP · CRM · SharePoint · databases L5 Knowledge, memory and state RAG · vector search · knowledge graph · durable workflow · run history Deployed on your cloud tenancy · EU data residency where required
The layers we assess, design and build. Most organisations already have L1 and parts of L5 — the gap is usually the control plane at L2.
  • Agents get identities. Not shared service accounts. Every action traces back to an agent, a version and a principal.
  • Tools are a registry, not glue code. Permissions, schemas and rate limits live in one place you can audit.
  • Policy sits above the runtime. You can change what agents may do without redeploying them.
  • Model choice stays reversible. A gateway keeps you free to move between providers and open models.

How an agent runs

Every action passes a gate.

An agent that can only read is a search box. An agent that can act needs a decision point between intent and consequence — and a record of what happened at that point.

Controlled agent execution loop A goal leads to a plan, then a proposed tool call, which passes through a policy gate. Permitted actions execute and are observed; actions needing sign-off go to a human approver; disallowed actions are denied, logged and escalated. Traces and evaluations feed back into the next run. Goal from a person Plan reason + retrieve Proposed action Policy gate Execute tool call Observe result + cost Human approval named, time-bound, logged Denied logged and escalated traces, evaluations and memory inform the next run allowed needs sign-off out of policy
The gate is where security, governance and business risk meet. It is the part most proofs of concept skip, and the reason most of them never reach production.

Here is that gate in a run trace — the record an auditor actually asks for.

invoice-exception-agent run 7f3a2c · v1.4
  1. 0.0splan3 steps proposedok
  2. 0.4sretrieve12 docs · policy corpusok
  3. 1.1stoolerp.invoice.lookupok
  4. 1.9stoolerp.payment.release · EUR 48,200held
  5. policy PAY-07 — releases above EUR 25,000 require a named human approver
  6. 6.2mapprovefinance.controllersigned
  7. 6.2mactpayment releasedok
  8. 6.2maudittrace sealed · retainedok
An illustrative agent run. Every step is checked against policy, held for a human where the policy says so, and written to an audit trail you can hand to an auditor.

Solutions

Agents built for real business work.

Enterprise knowledge agent

Answers from trusted internal sources, with citations and access controls intact.

Document processing agent

Reads, extracts, classifies and routes complex documents at volume.

Finance agent

Supports reconciliation, forecasting, variance analysis and reporting cycles.

Compliance agent

Assists KYC, AML screening, investigations and regulatory workflows.

Legal and tax agent

Research, document analysis, drafting support and matter workflow.

Security operations agent

Triages alerts, correlates evidence and prepares response actions for approval.

Customer service agent

Resolves requests and takes bounded actions across business systems.

Software engineering agent

Assists coding, testing, review, deployment and operational support.

AI and GenAI security

Control what AI does, not only what it says.

Agentic systems extend the attack surface past users, devices and applications to models, prompts, memory, tools, MCP servers, agent identities and autonomous actions. Content filtering alone does not cover any of it.

Agentic AI threat surface and control layers Seven threat vectors — prompt injection, tool misuse, memory poisoning, excessive agency, data exfiltration, model supply chain and identity spoofing — are intercepted by four control groups: input and output guardrails, identity and least-privilege tools, policy engine with human gate, and isolation with egress control. The protected agent runtime sits below, and outcomes are allowed and logged, held for approval, or blocked and alerted. THREAT VECTORS prompt injection tool misuse memory poisoning excessive agency data exfiltration model supply chain identity spoof AGENTICA CONTROL LAYER Input and output guardrails Injection detection, content and PII filters, untrusted-content tagging Identity and least-privilege tools Agent identity, scoped tokens, signed tool schemas, rate limits Policy engine and human gate Action allow-lists, value thresholds, approvals, kill switch Isolation and egress control Sandboxed execution, network allow-lists, data residency PROTECTED RUNTIME Agents Models Tools and MCP servers Memory and vector stores Systems of record versioned routed registered and scoped signed and expiring read/write bounded Every attempted action resolves to one of three outcomes Allowed, executed and logged Held for a named approver Blocked, alerted and escalated
We threat-model this surface for your environment, build the control layer, then try to break it before someone else does.
GenAI and agent security

Prompt injection, tool misuse, data leakage, unsafe autonomy and runtime controls across the agent lifecycle.

AI red teaming

Adversarial testing of models, agents, tools and workflows — including indirect injection through documents and email.

Secure-by-design build

Threat modelling before the first sprint, security requirements in the backlog, controls tested as part of delivery.

Existing security estate

Agent telemetry routed into your SIEM, identity into Entra ID, secrets into your vault. No parallel security stack.

Assume the agent will be attacked through its inputs.

Documents, emails, web pages, ticket comments and tool responses are all untrusted input to an agent that can act. We test exactly that path — and report findings in a form your CISO can take to the board.

Discuss a red team exercise

Governance and assurance

Evidence, not intentions.

Regulated organisations are increasingly asked to show which AI systems they run, how each one is classified, what controls apply and who signed off. We build that inventory and the evidence trail that keeps it current.

AI assurance lifecycle Five sequential stages: inventory AI systems, classify risk, apply controls, capture evidence, then review and attest, with the cycle repeating on every material change. 01 Inventory systems and agents 02 Classify risk and obligations 03 Apply controls and gates 04 Capture evidence and traces 05 Attest review and sign-off continuous — re-run on every material change
The same lifecycle satisfies most frameworks at once. The work is in making it run continuously rather than once, ahead of an audit.

EU AI Act

Risk classification and obligations

System inventory, risk tiering, technical documentation, human oversight and post-market monitoring.

ISO/IEC 42001

AI management system

Policy, roles, impact assessment and the management routine auditors expect to see running.

NIST AI RMF

Govern, map, measure, manage

A practical control structure that maps cleanly onto engineering work rather than sitting beside it.

ISO 27001 · GDPR

Security and data protection

Lawful basis, data minimisation, retention and the security controls your existing certification already assumes.

DORA · NIS2

Operational resilience

Third-party model risk, incident reporting and continuity for AI-dependent business processes.

Sector rules

Supervisory expectations

Banking, insurance and professional-services requirements translated into engineering controls.

Run and scale

The second year is the hard one.

Models change under you, prompts drift, costs creep and quality degrades quietly. Scaling AI is an operations problem long before it is a modelling one.

Evaluation you can trust

Golden datasets, regression suites and task-level scoring, so a model or prompt change is a measured decision rather than a hope.

Observability and tracing

Every run traced end to end — steps, tools, tokens, latency and failures — with alerting on the behaviour that matters.

Model routing and cost

Right-sized models per task, caching, budgets per agent and per team, and the ability to switch providers without a rewrite.

Capability transfer

Your engineers on the build, documentation that outlives us, and a handover that leaves the platform in your hands.

Industries

Cross-industry method, sector-specific execution.

The architecture generalises. The risk appetite, the regulator and the language of the business do not — which is why we staff each engagement with people who know the sector.

BankingFinancial servicesInsurance FinTechLegalTax and professional services GovernmentHealthcareEnergy TelecommunicationsManufacturingRetail Real estateHospitalityTransportation Supply chainEducation

Agentica intelligence

A working knowledge base for the agentic economy.

We are building a published layer of research, technology assessments, use cases and regulatory tracking — reviewed by a human before anything goes out.

in development

Agentic AI technology radar

Frameworks, runtimes and tooling scored adopt, trial, assess or watch.

in development

Use-case library

By industry and function, with value, complexity and control requirements.

in development

Agentica Labs

Reference architectures, demos and benchmarks from our own build work.

in development

Regulatory tracker

What changed in the EU AI Act timeline and what it means for your systems.

Team

The right expertise, assembled around the problem.

A focused core team and a curated network of AI engineers, architects, security specialists, data scientists and governance experts. We build the team around the engagement, not the other way around.

CEO and Founder

Michael Kilaras

Michael is an MIT-educated technology and AI transformation leader with nearly three decades of experience across banking, financial services, enterprise technology and entrepreneurship. His career includes technology roles at Goldman Sachs, serving as CTO of Eurobank Cyprus, and leading the Digital Factory, Core Banking and Business Applications functions at Bank of Cyprus. He combines enterprise architecture, digital transformation, AI, cybersecurity and regulated-industry delivery, supported by postgraduate engineering studies and executive AI education at MIT.

AI transformationEnterprise architecture Banking technologyDigital strategy
LinkedIn profile

Engineering and delivery

Technology team

Senior specialists who take agentic AI from strategy and prototype to secure, production-grade enterprise capability. Teams are assembled per engagement so the right mix of engineering, security, governance and industry knowledge is applied to the specific problem.

Agentic AI and LLM engineeringAgents, multi-agent systems, MCP, RAG, orchestration, model integration
AI, ML and data scienceMachine learning, analytics, data pipelines, evaluation, knowledge systems
Software and platform engineeringCloud-native applications, APIs, microservices, enterprise integration
Cloud, DevSecOps and MLOpsAzure, AWS, GCP, CI/CD, observability, LLMOps and AgentOps
Cybersecurity and AI securityThreat modelling, red teaming, identity, tool, data and runtime security
AI governance and GRCEU AI Act, ISO/IEC 42001, NIST AI RMF, privacy and responsible AI
Enterprise architectureEntra ID, ERP, CRM, SharePoint, databases, legacy modernisation
Product and industry expertiseUX, human-in-the-loop design, change adoption, domain knowledge
How we deliver Bring the right expertise to the problem — and keep delivery accountable, secure and tied to an outcome.

Start here

Turn AI ambition into enterprise capability.

Most engagements begin with a short, focused piece of work. Pick the one closest to where you are.

Office Stasinou 17, 9th floor Nicosia, Lefkosia 1060, Cyprus
Email contact@agenticatec.com We reply to every enquiry.
Book an AI discovery workshopHalf a day with your team to find the work worth doing Request a readiness assessmentData, platform, security and capability, scored honestly Scope an agent proof of conceptOne real workflow, built to production standards Review AI security and governanceThreat model, control gaps and an evidence plan