Agentic AI Pillar 1. Safety and Trust

First in a six-part series on the pillars of Enterprise Agentic AI for Agent & Multi-Agent Team Design building on the launch article published here last month. This article is co-authored by Rob Price , co-founder of Futuria , and Patricia Shaw , CEO of Beyond Reach Consulting Limited , with occasional assistance from ChatGPT and Claude.

Introduction: Safety & Trust is the First Design Principle

In the shift from task-based automation to Agentic AI, enterprises are enabling digital systems that act with greater autonomy and initiative. These agents — sometimes working solo, sometimes in collaboration in multi-agent teams— must be trusted to act reliably, ethically, and transparently.

This isn’t just a technical challenge. It’s a design challenge. And it’s not simple.

This article explores the first and foundational pillar of Enterprise Agentic AI: Trust, focusing on the standards, guidance, and responsibilities needed at the agent and team level. We’ll look at how trust is earned through clarity of roles, consistency of design, and alignment with ethical and organisational values — long before an agent is deployed into production.

Later in the series, we’ll explore how Trust links to the other five pillars — from enterprise-level Control to Team Models, Quality, Infrastructure and Commercialisation — but this article focuses on getting the foundations right.

Trust Through Systematic Agent and Multi-agent Governance

The transition from task-based automation to Agentic AI represents a fundamental shift in how enterprises approach digital transformation and governance. Unlike traditional automation systems that follow pre-determined scripts, agentic AI systems demonstrate a means of autonomous decision-making, goal pursuit, and adaptive behaviour. This evolution demands a governance-first approach where safety and trust are not just wishful thinking but systematically designed, developed, measured, monitored, and maintained.

Drawing from insights learned from practising the comprehensive Safer Agentic AI Foundations framework, Corporate Digital Responsibility, and the Values Based Engineering method of applying IEEE/ISO/IEC 7000 standards, this article explores how organisations can establish robust governance operating models that create sustainable trust through systematic standards, clear accountability, and embedded safety measures. Trust becomes not just a design principle, but a measurable outcome of good governance.

1. Standardisation: Designing for Confidence and Clarity

Without a shared language or approach to agent development, organisations risk a Wild West of duplicated effort, incompatible agents, and misaligned outcomes – in essence low code/no code sprawl, but magnified. To mitigate that we would suggest thinking about how you intend to structure your agents:

A.    Unified Agent Design Patterns

First things first is to establish standardised agent types and ways of working:

• Task Agents: Specialists for repeatable jobs
• Coordinator Agents: Orchestrators or brokers across workflows
• Interface Agents: Human-facing, responsible for safe interaction

Standardisation should cover:

• Naming conventions
• Decision boundaries and escalation points
• Interface contracts (APIs, memory use, permissions)
• Logging, explainability, and test scaffolding

This isn't about rigid templates. It’s about consistent mental models, so developers, architects, and oversight teams all understand what kind of agent they’re dealing with.

Risk-based design classification should determine which standards apply to which agents.

High-risk agents handling financial transactions, healthcare decisions, or safety-critical operations require additional design safeguards and mandatory human oversight points to ensure, at all times, rigorous regulatory compliance.

Medium-risk agents serving customers or processing sensitive data need standard safety patterns, while low-risk internal productivity agents can operate with streamlined trust frameworks.

This risk classification also determines how safety requirements are “inherited” when agents work in multi-agent settings such as teams—with the agentic team's overall risk level being classified and determined by its highest-risk agentic member.

B.     Multi-Agent Team Topologies

As complexity grows, agents work in agentic teams — intentionally or iteratively.

It therefore becomes essential to define patterns for:

• Task decomposition (planner–executor)
• Validation and feedback (actor–critic)
• Parallelism vs. hierarchy (flat swarm vs. lead agent)

Pattern libraries here act as design shortcuts, reducing risk and promoting reuse.

Multi-agent team safety introduces unique challenges that don't exist with single agents. Teams require conflict resolution protocols when agents have competing goals or receive contradictory instructions. Safety protocols must govern agent handoffs, ensuring context and safety state transfer correctly between team members. Most critically, teams need safeguards to prevent agents from amplifying each other's errors or biases—a phenomenon that can lead to cascading failures.

Consider incorporating dedicated "safety agent" roles within teams—agents whose primary responsibility is monitoring team behaviour and intervening when safety thresholds are exceeded. For high-risk decisions, design consensus mechanisms that require multiple agent agreement, preventing any single agent from taking dangerous actions unilaterally.

2. Guardrails and Boundaries: Safety Without Paranoia but with Governance

Designing for trust doesn’t mean overly constraining agents. It means embedding clear operational boundaries that prevent harm, building in ex-ante safety mechanisms that allow for clear monitoring of alignment and governance and provide for safe recovery when things go wrong.

Embedded Guardrails can include:

• Role-specific action limits
• Controlled access to external systems or data
• Safety interlocks (human approvals, confirmation loops)
• Edge-case escalation (to humans or higher-order agents).

These should be designed-in, not retrofitted. And they should be testable — both in isolation and in simulation environments.

Safety-first design patterns should be fundamental to agent architecture. Agents must be designed with explicit fail-safe vs. fail-secure behaviours—determining whether they should stop operating or continue with restricted capabilities when systems fail. Built-in safety checks should prevent high-risk actions like unauthorized financial transactions, data deletion, or external communications without appropriate verification.

Emergency stop mechanisms must be designable into agents from the ground up, not retrofitted later. These should include both automated triggers (when safety thresholds are exceeded) and manual intervention capabilities that can be activated by humans or other safety systems.

3. Responsibility and Ethical Alignment: Designing for Values

Agentic systems increasingly make decisions on our behalf. Responsibility for the impact and outcomes of agents is non-negotiable and therefore requires clearly articulated delegated authority matrices with reasoning behind the human decision to delegate before putting an agent to work.  This process then must be undergone again algorithmically for the agent(s). Responsible Agent Design incorporates:

• Transparent reasoning: Can we audit the logic and steps?
• Explainability: Can the agent, or agent team, show its work?
• Fallbacks and accountability: Who (or what) is responsible when something fails?

These principles should draw from established AI ethics frameworks (e.g. OECD, EU AI Act, IEEE), but they also need to be interpreted in context: what does "ethical" mean in the context of the agentic system and in the context of its domain(s) of operation? What are the values of the organisation and of those who are going to be impacted and affected? How does the operation of the AI agent(s) align with those values?

Things to consider when establishing the controls and mitigations will be:

• What’s the right balance of speed vs. caution?
• What risks are worth taking?
• What goals can never be compromised?

Value-Based Engineering is an emergent approach to ensure that systems (in this case agents and/or multi-agent systems) should reflect societal and ethical values which are accepted and acceptable to the relevant stakeholders.  It’s approach brings about a set of system controls and mitigations and counter-controls to ensure that the systems stay without value boundaries.

Engineers need guidance — and organisational buy-in — to embed these values early in the build process, ideally at the inception stage but also at the development stage, whether the build process is conducted by the agents themselves, or the platform on which the agents are configured.

Human-Agent Interaction Safety

Not only should agents and multi-agents be designed and developed in alignment with human-centric values, stakeholder expectations and within clearly recognised boundaries, but how the agentic systems are designed for human interaction is an essential component of trust and safety.  When designing for human-agent interaction, consider:

• How does agentic boundaries and threshold monitoring create potential dependencies and unhealthy levels of engagement or disengagement?
• What safeguards are in place to help identify where the AI agents detect patterns of use, which could impact on human monitoring and governance?
• Are the intervention protocols designed for the AI agents or human reaction optimality? What is the range that has been set for intervention protocols i.e. gentle usage reminders, alerts and alarms to garner permission, or restricted access?
• How is authority managed? Are there clearly established hierarchies of control?
• Is the agentic system set to accurately translate human intent or human feedback?
• Are cultural and contextually sensitive differences appropriately gaged and addressed to maximise human-agent interaction?

When agents interact with humans, additional safety considerations become paramount. Clear disclosure requirements must define when agents must identify themselves as AI—particularly in customer service, counselling, or advisory contexts. Special safety boundaries are needed when agents interact with vulnerable populations, including children, elderly users, or individuals in distressed states or socially sensitive contexts.

Design patterns must maintain human agency and prevent over-reliance on agents. This includes building in natural break points for human reflection, avoiding manipulative persuasion techniques, and ensuring humans retain meaningful control over important decisions. Safety protocols should also govern how agents handle sensitive personal information shared in conversations, with automatic escalation triggers when human emotional or psychological safety may be at risk.

4. Core Design Documents and Artefacts

To embed trust organisation-wide, individuals need practical guidance, not just principles. We would propose that a mature enterprise approach should ultimately include:

Agent Design Guidelines

• Define roles, responsibilities, lifecycle stages
• Clarify expectations for memory, learning, observability
• Include prompts for ethical risk review

Multi-Agent Team Playbook

• Patterns for collaboration, escalation, and arbitration
• Templates for coordination protocols and shared goals

Process Blueprints

• Where do agents fit into existing workflows?
• Who owns the process — and who owns the outcome?

Value Codification Process

• Processes for how values are identified, prioritised, and codified into the agentic system
• Document what those core values are and state justifications for how these are integrated into decision processes
• Identify how testing of the values codex is conducted, and what benchmarks demonstrate that values are aligned.

These artefacts help ensure that whether you're in engineering, design, compliance or ops, you’re building on a shared foundation of trust.

This also lays the groundwork for later Control mechanisms — such as evaluating whether a new agent is even needed, or how to decommission one.

5. The Role of Safety and Trust in the Broader Agentic AI Architecture

Safety and Trust doesn’t exist in a vacuum. They must connect with — and support — other pillars of Enterprise Agentic AI:

• Control: Safety and Trust at the agent level enables higher-level governance and oversight (but we’ll explore that in the next article).
• Quality: Standards and guardrails support performance evaluation and continuous improvement.
• Hybrid Teams: Clear boundaries and ethics are vital when agents collaborate with humans.
• Infrastructure: Design standards support scalable, maintainable infrastructure choices.
• Commercial Models: Safe and Trustworthy design unlocks confidence to invest, purchase, or license agentic capabilities.

This is why Safety and Trust is the first pillar — without it, the others can’t function safely or sustainably.

Conclusion: Safety and Trust are the First Real Design Decisions

Agentic AI won’t succeed at enterprise scale through experimentation alone. It must be designed with intent, aligned to values, and supported by standards, guidance and guardrails that build safety and trust into every layer — starting from the agent up.

In the next post, we’ll zoom out to explore Control: how to govern agentic systems at scale, without losing flexibility or innovation.

But it all starts here — with Safety and Trust, by design.

References

Trish and Rob have previously collaborated on papers for IEEE and academic papers around Digital Responsibility.

1. Safer Agentic AI Foundations, Volume 2 – Issue 1, March 2025
2. Previous collaborations https://store.accuristech.com/ieee/standards/ieee-white-paper?product_id=2922055&vendor_id=11827#full and https://corporatedigitalresponsibility.net/blog-1/f/raising-the-global-recognition-of-cdr
3. Corporate Digital Responsibility Manifesto, https://www.corporatedigitalresponsibility.net and https://corporatedigitalresponsibility.net/cdr-manifesto
4. ISO/IEC/IEEE 24748-7000:2022 and Spiekerman, Sarah, Value-Based_Engineering_A_Guide_to_Building_Ethical_Technology_for_Humanity, https://www.researchgate.net/publication/369078837
5. AI for the Rest of Us meet-up June 2025 report https://www.linkedin.com/posts/rob-price-4a44884_meetups-responsible-aiagents-activity-7341763195696496640-m9JB?utm_source=share&utm_medium=member_desktop&rcm=ACoAAADIMLwB3sMRAwvp8vwrXA7QJGoNpEopYac
6. The Usher-Middleton scale for Multi-Agent Teams https://www.linkedin.com/posts/rob-price-4a44884_multiagentteams-agenticai-activity-7340685962546454528-xHXy?utm_source=share&utm_medium=member_desktop&rcm=ACoAAADIMLwB3sMRAwvp8vwrXA7QJGoNpEopYac
7. Futurise Podcast https://open.spotify.com/show/3BFEdGmKB1qiQptZf37aSc?si=48f3368a4dcb453a
8. Scaling Multi-Agent Teams https://www.linkedin.com/pulse/scaling-multi-agent-teams-rob-price-hmsae/?trackingId=jBLQGeV2zOubHRsWps7Qag%3D%3D
9. Pillar 2 - Control & Governance https://www.linkedin.com/posts/rob-price-4a44884_quality-activity-7360952007643328512-RcYg/
10. Pillar 3 - Quality & Performance https://www.linkedin.com/posts/rob-price-4a44884_agenticai-activity-7363510347867029504-2taM/