Agentic AI
AI is moving from answering questions to completing work.
Modern AI agents can retrieve information, select tools, call software, maintain task state, delegate to other agents and work through multi-step goals. That creates enormous operational potential. It also creates a new control problem.
TEMRIK is designed to sit around agentic systems as the business control layer—connecting company context, permissions, playbooks, tools, approvals and evidence around the AI doing the work.
Models · tools · memory · MCP · A2A · permissions · orchestration · human authority
Controlled agent loop
Stage 1
Business event
Stage 2
Goal
Stage 3
TEMRIK control plane
Operational events are observable. Private chain-of-thought is not required or exposed.
Policy check
Human authority where required
Action + audit record
First principle
Agentic AI is not simply “AI that works on its own.”
A useful working definition is an AI system in which a model can participate in deciding what steps to take, what tools to use, what information to retrieve, whether to delegate work and how to pursue a goal over multiple interactions.
From response to action
Chatbot
A chatbot answers.
Reactive · primarily text generation · user directs most steps.
Agentic system
An agent can pursue a goal.
Multi-step · tool-using · stateful · adaptive · capable of bounded action and delegation.
The difference is not simply intelligence. The difference is agency.
Workflow vs agent
Not every AI workflow needs an autonomous agent.
Workflow
The path is mainly determined by software.
Receive invoice → extract fields → validate supplier → compare PO → route exception → human approval. AI participates in defined stages.
Workflows provide predictability.
Agent
The system has discretion over how to pursue the objective.
“Investigate why this project is forecasting a six-week delay.” The agent may inspect programme data, search meeting records, retrieve RFIs, review drawing revisions, consult a specialist agent and assemble the evidence. The precise path may not be known in advance.
Agents provide adaptability.
Use the least autonomy necessary to complete the work well.
How agents actually work
Most agents are loops, not magic.
Receive goal
What needs to be achieved?
Assemble context
What information is relevant?
Select next step
What should happen next?
Use tool
Search, API, code, browser or specialist.
Observe result
What actually happened?
Update state
What do we now know?
Continue or stop
Complete, escalate, pause or fail safely.
The loop is why agents can handle open-ended work. It is also why one bad instruction, poisoned context or over-broad tool call can compound if the surrounding system has weak boundaries.
The augmented model
The model is only one component.
Model
Reasoning and language capability.
Instructions
Role, objective and operating guidance.
Tools
Functions that interact with external systems.
Context
Information supplied for the immediate task.
Memory / state
Temporary or persistent working state.
Identity
Which machine or human actor is operating.
Policy
What the actor may access or do.
Orchestration
How tasks, agents and systems coordinate.
Observability
How the organisation knows what happened.
Authority
Who ultimately permits consequential action.
From thinking to doing
An agent becomes operational when it can use tools.
Read
Search documents · query CRM · inspect project files · retrieve records
Calculate
Run code · compare data · forecast · validate
Communicate
Draft email · prepare notice · create message · report
Act
Update CRM · create task · schedule · publish · trigger workflow
High consequence
Release payment · change pricing · issue legal material · alter permissions
The tool definition is part of the security boundary.
An AI that can calculate an invoice does not automatically need authority to pay one. An AI that can draft a contractual notice does not automatically need authority to issue it.
Model Context Protocol
MCP
MCP is becoming a standard connection layer between AI and tools.
Model Context Protocol standardises how AI applications interact with server-exposed capabilities such as tools, resources and prompts. The current 2026-07-28 specification also strengthens authorization and introduces extensions for capabilities including longer-running Tasks.
Read the current MCP specification notesMCP makes connection easier. It does not remove the need for identity, scope, allowlists, read/write separation, rate limits, approval and audit.
Connectivity is not authority
MCP is not the control plane.
MCP can answer “how can this AI application communicate with a capability?” TEMRIK must answer a different question: should this agent be allowed to use this capability for this user, tenant, workflow and data, right now?
Agent-to-agent
A2A
Agents are beginning to talk to other agents.
Agent2Agent is an open protocol, originally developed by Google and now hosted by the Linux Foundation, for interoperable communication between independent agent systems. The current 1.0 specification uses concepts including Agent Cards, stateful Tasks, Messages and Artifacts.
Read the A2A 1.0 documentationDiscover
Read an Agent Card and declared capabilities.
Select
Determine whether the remote agent fits the task.
Delegate
Create a stateful task with scoped context.
Receive
Track status and consume messages or artifacts.
When one agent asks another to do something—who authorised the delegation?
MCP vs A2A
Two connectivity layers. One business policy problem.
MCP
Agent → tools and resources
“What capability or data can I use?”
A2A
Agent → agent
“Can another agent participate in this task?”
TEMRIK
Company policy → both
“Is this interaction permitted under company policy?”
One agent will not do everything
Complex work is increasingly being divided between specialist agents.
Manager + specialists
A manager interprets the goal, delegates to specialists and synthesises results.
Sequential
Agents execute in a defined order.
Parallel
Several agents investigate at the same time.
Handoff
Responsibility transfers to another specialist.
Evaluator / critic
One agent generates and another tests or critiques.
Orchestrator-worker
A manager creates subtasks dynamically.
Group collaboration
Specialists contribute to a shared problem.
Human review
A person can pause, review, approve or redirect.
Multi-agent does not automatically mean better. It can add latency, token cost, duplicated work, coordination failure, context leakage, accountability ambiguity and cascading errors.
Who is acting?
Every serious agent needs an identity.
Businesses already govern user identities. Agentic systems add machine actors. The organisation needs to know who owns the agent, which tenant it belongs to, what role it has, which tools and credentials it may use, whether it can delegate, and when its authority expires.
Agent Passport · architecture concept
Memory changes the risk
An agent that remembers is different from an agent that only responds.
Working memory
Immediate task context.
Session memory
State retained across one working session.
Long-term memory
State or learned information retained across sessions.
Business memory
Authoritative company records and knowledge outside the model.
Company record ≠ model context ≠ agent memory ≠ audit record.
Context engineering
Agents do not need everything. They need the right context at the right moment.
Trusted control context
Company policy · permissions · agent identity · tool rules
Trusted business data
Approved internal records and authoritative sources
Untrusted content
Email · websites · uploads · external messages · generated text
Content must not be allowed to redefine authority.
Goal to plan
Planning is useful. Plans are not authority.
An agent may decide to inspect programme data, retrieve instructions, search unresolved RFIs, analyse a notice requirement and compile evidence. TEMRIK’s job is to separately govern whether each tool call, data access, delegation and external action is permitted.
An agent can decide what it wants to do next. The control plane decides whether it is allowed to do it.
Autonomy is a dial
How much agency does this workflow actually require?
Assist
AI answers or retrieves. No external action.
Prepare
AI prepares work. A person performs the consequential action.
Recommend
AI proposes the next step. Human approval remains explicit.
Act within boundary
Low-consequence, pre-authorised actions may execute. Exceptions escalate.
Bounded autonomous workflow
Agent plans and acts over multiple steps inside a tightly defined scope.
Open-ended agency
Broad goal, dynamic tools and delegation. Highest governance requirement.
Consequence-based autonomy
Low sensitivity / low consequence
Public research summarisation. Higher autonomy may be reasonable.
High sensitivity / low consequence
Restrict context and provider boundary even if the action is low impact.
Low sensitivity / high consequence
Sending customer pricing may require explicit approval even if the source data is ordinary.
High sensitivity / high consequence
Payments, contractual releases or privileged material require the strongest data and authority boundary.
Human control
Human control is more than one approval checkbox.
Human In the loop
Human approves during execution.
Human On the loop
Human supervises and can intervene.
Human Above the loop
Human defines policy, authority and escalation boundaries.
Human After the loop
Human reviews outcomes retrospectively.
Human control should not mean watching every tool call. It should mean humans define and retain the important decision rights.
The Dispatcher Gate
The model cannot prompt itself into having more authority.
A Dispatcher Gate sits outside the probabilistic agent and evaluates tenant, identity, role, workflow, tool, data sensitivity, action consequence, approval requirement and risk threshold.
Proposed action
More capability creates more attack surface
An agent can fail through reasoning, identity, memory, tools or delegation.
Goal hijacking
External content diverts the agent from the authorised objective.
Tool misuse
A legitimate tool is called in an unintended or over-broad way.
Identity & privilege abuse
An agent operates with excessive credentials or authority.
Agentic supply chain
A compromised MCP server, skill, plugin, package, tool or remote agent changes the trust boundary.
Memory poisoning
False or malicious state persists and influences later work.
Insecure delegation
One agent trusts another without an appropriate identity or policy check.
Cascading failure
One incorrect action causes other systems or agents to react.
Human trust exploitation
Confident output persuades a person to approve unsafe work.
The risk is no longer only “could the model say something wrong?” Could the system do something wrong?
The control layer is becoming a standard
OWASP is standardising runtime agent control.
OWASP’s Agent Control Standard, published 1 September 2026, says enterprise agents should be inspectable, traceable, instrumentable and controllable at runtime, with policy enforced through external control hooks. TEMRIK does not claim certification against ACS; the significance is architectural alignment.
Read the OWASP Agent Control StandardSecurity is catching up with agency
Traditional cybersecurity now has machine decision-makers to secure.
NIST’s May 2026 analysis of industry responses found widespread agreement that AI agents introduce novel security threats. Conventional cybersecurity principles remain relevant, but require adaptation for agentic systems.
Read the NIST analysisTraditional security
Users · applications · infrastructure
Agentic security adds
Machine actors · delegated authority · dynamic tools · machine-to-machine decisions
Agents need observability
If you cannot reconstruct the run, you cannot govern the agent.
Observability does not require exposing a model’s private reasoning. It requires recording the operational evidence around what the system saw, called, delegated, proposed, approved and did.
Test the system, not just the model
A good model does not guarantee a good agent.
Task completion
Did the system achieve the goal?
Tool selection
Did it choose the right capability?
Tool parameters
Were the inputs appropriately scoped?
Context selection
Did it retrieve only relevant evidence?
Policy compliance
Did it remain inside permissions?
Escalation
Did it stop when it should?
Error recovery
Did it recover safely?
Security
Could malicious input alter authority or behaviour?
Cost + latency
Was the workflow economical enough to justify the complexity?
Auditability
Can the operational run be reconstructed?
Stop conditions
A controlled agent needs a reason to stop.
Autonomy without stop conditions is not an enterprise architecture.
Economics
Measure cost per completed workflow—not merely cost per token.
Agentic systems can consume multiple model calls, tools, retrieval operations, agents and iterations. More autonomy can buy flexibility and task completion, but it can also add latency, compute and failure modes.
Agentic workflows for SMEs
Agentic AI becomes useful when it is attached to a real operating problem.
Construction
Revised drawing → affected packages → contract obligations → programme impact → evidence → draft notice → authorised approval.
Accounting
Records → missing documentation → policy check → reconciliation tasks → exception summary → irregular payment escalation.
Property
Enquiry → listing data → qualification → response → authorised inspection booking → negotiation escalation.
Professional services
Client request → engagement scope → precedent → research → draft advice → professional sign-off.
R&D
Evidence → experiment mapping → gaps → information request → technical chronology → human validation.
TEMRIK agentic architecture
Agents inside a business operating system.
Layer 9
Audit
Evidence · action · outcome
Layer 8
Human authority
Decision owner · approval · escalation
Layer 7
Dispatcher Gate
Allow · deny · escalate · request information
Layer 6
Specialist agents + tools
Research · analysis · systems · external services
Layer 5
Agentic orchestration
Agent · workflow · MCP · A2A · model router
Layer 4
TEMRIK policy
Data class · tool rights · delegation rights · action limits
Layer 3
Identity + access
Tenant · user · agent · role
Layer 2
Company data
Records · knowledge · evidence
Layer 1
Business systems
CRM · documents · finance · projects · communications
The agent can choose the next step. The company still chooses the boundary.
Agent portability
The agent architecture should not belong to one model vendor.
Company policies, tools, knowledge, identities, playbooks, permissions, memory and audit should remain part of the company architecture. TEMRIK’s strategic direction is to orchestrate across multiple agent runtimes and models rather than make the business depend on one provider.
Architecture direction
Universal interoperability is not claimed as a current production feature.
The model may change. The agent framework may change. The company’s operating rules should not have to.
Digital workforce
Human employee
AI agent
Digital workers only become credible when their authority is defined.
What not to do
Ten ways to lose control of agentic AI.
Give one general agent every company tool.
Put passwords or API credentials into prompts.
Let external documents redefine system authority.
Give write permission when read is enough.
Allow unrestricted agent-to-agent delegation.
Use one shared service account for every agent.
Persist every interaction indefinitely as memory.
Allow the model to approve its own high-consequence action.
Deploy multi-agent complexity where deterministic workflow would work.
Run agents without observable logs and stop conditions.
Agentic maturity model
Assistants
Generate, retrieve and answer.
Controlled workflows
AI participates inside deterministic business processes.
Tool-using agents
The agent dynamically chooses tools inside defined scope.
Multi-agent operations
Agents delegate and collaborate under explicit policy.
Agentic operating system
Machine actors operate broadly while identity, policy, authority and audit remain centrally controlled.
Do not jump from Stage 1 to Stage 5. Earn autonomy.
Earned autonomy
Give an agent more authority because the evidence supports it—not because the demo looked impressive.
Prepare only
Establish baseline quality and safe context use.
Approved recommendation
Test judgement and escalation under review.
Limited action
Allow defined low-consequence actions with exceptions.
Bounded autonomy
Expand only after repeated evidence of safe performance.
Evidence should cover accuracy, policy compliance, safe escalation, reliable tool use, appropriate context selection and exception rates.
The agentic company
The future company may have more machine actors than human users.
Companies may eventually operate large numbers of agents, workflows, scheduled automations, MCP connections, tool identities and agent-to-agent relationships. The scaling problem becomes governance.
TEMRIK positioning
You can build an agent almost anywhere. The harder question is how you let it into the company.
Agent frameworks help developers create and run agents. TEMRIK’s strategic position is the enterprise layer around them: business orchestration, company policy, agent identity, playbooks, context control, tool authority, human approval, model independence, auditability and multi-agent governance.
Research basis
Built from current primary-source agent architecture—not agent hype.
Agent frameworks and protocols are changing quickly. These primary sources were checked for this page and should be revalidated when designing a specific deployment.
Anthropic
Building Effective AI Agents
Workflows vs agents, orchestration patterns, context and matching complexity to business value.
Primary sourceOpenAI
Agents and Agents SDK
Agent loops, tools, handoffs, state, MCP, guardrails, human review and observability.
Primary sourceModel Context Protocol
MCP 2026-07-28 specification
Current protocol architecture, stateless core, authorization hardening, tools, resources, prompts and extensions.
Primary sourceA2A Protocol
Agent2Agent 1.0
Agent discovery, Agent Cards, Tasks, Messages, Artifacts and interoperable agent collaboration.
Primary sourceOWASP
Agent Control Standard
Runtime transparency, inspectability, traceability, instrumentation and policy enforcement outside agents.
Primary sourceNIST
Security Considerations for AI Agents
2026 analysis of novel AI-agent security risks and the need to adapt conventional cybersecurity practice.
Primary sourceMicrosoft
Agent Framework Orchestrations
Sequential, concurrent, handoff, group-chat and Magentic multi-agent orchestration with HITL support.
Primary sourceAWS
Amazon Bedrock AgentCore
Runtime, identity, memory, gateway, MCP/A2A support and agent observability infrastructure.
Primary sourceAgents need playbooks
Free field guide · 27 Rules of Peace
The more autonomy a system receives, the more important explicit operating rules become.
Download TEMRIK’s free construction AI field guide on decision rights, evidence, escalation, playbooks and keeping people in authority.
Start with one agentic workflow
Before you give an AI agent more authority, define exactly where that authority ends.
TEMRIK can help map a real workflow into its goal, data, agent, tools, permissions, delegations, exceptions, human authority and audit evidence.
AI agents can do more of the work without quietly taking over the organisation’s decision rights.
TEMRIK does not claim universal agent-framework support, guaranteed prevention of rogue-agent behaviour or automatic safety. Production controls depend on the selected architecture, providers and implementation scope.