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09 Jun 2026

Deploying AI Agents You Can Trust

Key recommendations to safely scale agentic AI

This article outlines how to ensure that fully autonomous agents only use validated, high trust data and how to effectively govern the memory lifecycle. It also explores how to capture tamper proof records of agentic decisions that can support audits and investigations, as well as how to properly validate inputs and increase the resilience of AI agents.

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This page is part of A C-suite guide to capturing the potential value of AI.

To achieve these data, memory and resilience objectives, organisations can translate governance principles into four practical controls that teams can apply immediately to reduce risk and scale agentic AI safely. Together they form the backbone of this article and provide a roadmap for embedding trustworthy agentic AI across your operations.

  1. Map data trust to action authority: Tier inputs and bind trust levels to permitted action scopes so only validated, high‑trust data enables full autonomy. 
  2. Govern memory lifecycle: Treat memory as a governed asset with named stewards, clear retention rules and expiry dates, and consent‑driven purges. 
  3. Ensure auditable decision traces:  Capture tamper‑proof decision records (trace ID, inputs and sources, tool calls, memory state, final action) so you can replay decisions and support audits and investigations. 
  4. Validate inputs and build resilience: Treat all incoming signals as untrusted until checked and add safety checks so small errors do not scale: apply guardrails, test changes on a small slice first, automatically pause when risk spikes, and use human review for edge cases. 

Common pitfalls of scaling autonomous AI

Through our work with clients, we have identified the factors that are most likely to derail the large-scale deployment of agentic AI. In the absence of robust data, memory and boundary controls, autonomous agents can make poor decisions, giving rise to major operational, compliance and reputational risks. Below, we outline three common pitfalls and how organisations can avoid them.    

In some cases, larger models can recognise contradictions or missing preconditions, and if they spot incomplete or inconsistent inputs, they may be tuned (via guardrails/refusal policies) not to act. However, this behaviour, which is task and environment dependent, varies with prompts, fine tuning and runtime thresholds. For agentic systems, it is safer to assume that input defects will be amplified: a more capable model may execute a flawed plan faster and with more confidence. Indeed, focusing investment on scaling models, rather than data quality, can simply mean faster, wider failures. Better to prioritise data provisioning, input validation and escalation controls before expanding autonomy or model complexity.

Persistent memory that grows without clear ownership, retention rules or regular review can cause gradual drift, hidden bias and compliance gaps. As an agent’s memory shapes its future behaviour, treat memory like any other governed asset: assign a named owner, set “time‑to‑live” and archival rules, and revalidate or purge records on a schedule.

If agents can pull any data or call any tool without limits, they are more likely to produce outputs that are wrong, insecure or hard to defend — and, in regulated contexts, can corrupt systems of record and regulated outputs by inserting unverified, non-authoritative, or sensitive content into policies, financial statements, contracts, audit logs, or regulatory filings. The result is “official” records that are inaccurate, non-compliant, or disclose confidential information. These risks can be mitigated by narrowing the retrieval scope, enforcing simple boundary checks around tools (so every call records provenance and sensitivity), and keeping all actions observable and auditable. Use staged rollouts and human checkpoints for high‑risk tasks.

Taken together, these pitfalls show that scaling agentic autonomy without disciplined data, memory and boundary governance is very risky. However, with the right controls in place, organisations can scale confidently and capture the operational benefits of reliable autonomous agents.

Best practices: the four pillars of trustworthy agentic AI

With strong data, memory and boundary controls in place, autonomous agents deliver reliable outcomes. Here are four recommendations for how to embed practical controls that will build operational, compliance and reputational confidence, and where organisations should focus to scale safely. 

An agent’s level of autonomy should vary with the quality of its inputs. By tiering inputs and binding those tiers to permitted action scopes, validated, high trust data can enable fully autonomous operations, while data from less trusted sources is only employed for advice, or actions that require approval. For example, an accounts payable agent could be configured to auto pay invoices up to a certain amount when the invoice, purchase order and goods received note all match in the enterprise resource planning (ERP) system. But if the supplier master data lists multiple bank accounts or contains conflicting details, the agent may be limited to drafting a payment. Ideally these checks are implemented ‘outside’ of the large language models, as hard conditions for decision making, so that they cannot be influenced by e.g. prompt injection.

Treat an agent’s memory as an asset — give it an owner, an expiry date and clear rules on what is kept. Separate short lived context (for a specific task) from durable learnings (that affect future behaviour), and delete or re check content on a schedule.

Every meaningful action should leave a clear, tamper proof record of what the agent observed, decided and did — so you can replay, explain and defend it. Capture a trace ID, inputs and sources, tools used, the state of memory and the final action. This can, for example, be achieved using dedicated LLM observability tools such as LangWatch, or as part of an MLOps platform that provides logging, traceability and governance. These measures will help ensure an organisation can meet their regulatory obligations, which are likely to increase as policymakers seek to introduce more safeguards around AI.

Treat every signal as untrusted until it has been checked, and add speed bumps so small mistakes don’t become systemic. Use guardrails (policy and fact checks), and test changes with a small audience first. If error or complaint rates spike, pause automation with clear paths to human review. For example, a supply chain agent that automatically makes replenishment orders could be configured to switch to making recommendations when forecast variance exceeds a pre-set threshold or supplier risk rises. In this case, a simple speed bump could be to run a “canary phase” – the agent would only auto-order a small slice, then require approval.

Taken together, these practices make autonomy predictable: data gates determine what an agent may do, memory is effectively governed, decisions are explainable, and resilience controls stop minor issues from scaling into major incidents.

Scale with confidence through proportionate controls and clear ownership

In summary, it is important to recognise that trustworthy agentic AI depends more on disciplined data and operational controls than on model size alone. Implementing the four pillars outlined in this article — mapping data trust to action authority, governing memory, keeping auditable decision records and validating inputs with safety checks — materially reduces operational and compliance risk.

Act now: assign owners for data and memory, pilot an agent with end-to-end controls and auditable decision records, then formalise funding and governance to scale what works for your organisation.

To find out more about how to deploy AI agents you can trust, contact our experts listed below.

Get in touch

Naser Bakhshi

Naser Bakhshi

Partner
+31882883874
Sjors Broersen

Sjors Broersen

Director
+31882885790

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