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Start with a workflow and clear boundaries
Buy for a specific operational outcome, not a general promise of automation. Identify the current baseline, the people who handle the work, and the exceptions the proposed agent is expected to manage. Define what counts as successful completion and who owns unresolved cases.
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- Which workflow is in scope, and what measurable outcome should improve?
- What may the agent do on its own, what requires approval, and what is prohibited?
- What is the impact of an incorrect answer, a delayed action, or an unintended tool call?
- What should happen when the agent is uncertain, encounters an exception, or cannot reach a system?
Use business value and failure impact to choose the first deployment. Requirements should reflect the workflow’s risk: an agent that drafts internal notes does not need the same authority as one that changes a customer record or initiates a consequential transaction. Buyer guidance also cautions that ordinary feature comparisons can miss accountability, exception handling, and cross-system consequences (TechTarget’s vendor questions).
Verify agent identity, permissions, and human control
Establish how the platform identifies each agent and limits what it can do. Shared human credentials make it harder to determine which actor took an action and under whose authority. NIST’s August 27, 2026 guidance argues that agents need their own identifiers, credentials, and entitlements bound to the identity of the user or system operating them (NIST Cybersecurity Insights).
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Ask a vendor to demonstrate these controls in the product, not just describe them:
- A distinct, attributable identity for each agent and a named organizational owner.
- Least-privilege access for each tool, data source, and permitted action.
- Delegation records showing whose authority the agent is using and for what purpose.
- Credential and key issuance, rotation, expiry, and revocation.
- Approval gates for consequential actions, plus a reliable way to stop or suspend an agent.
- Audit records linking the request, policy decision, agent identity, tool call, result, and any human approval.
NIST’s agent identity concept paper raises identification, authentication, key management, least privilege, delegation, binding agent identity to human identity, auditability, and non-repudiation as design questions. It also asks how teams can establish least privilege when an agent’s actions may not be fully predictable and make actions and intent verifiable (NIST concept paper). These are concerns and design directions, not proof that every platform or emerging protocol already solves them.
Map data flows and privacy terms
Trace what happens to information throughout the system, not only what appears in a prompt. Include retrieved records, tool inputs and outputs, agent memory, telemetry, evaluation data, and backups. Ask the vendor and the teams responsible for each connected system to establish:
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- How tenant isolation and sensitive data are handled, including what can be inferred by aggregating information.
- Where data is processed and stored, and what retention, deletion, export, and residency controls apply.
- Whether customer content is used for model training, fine-tuning, service improvement, or by subprocessors.
- Which embedded models, tools, connectors, and other third parties can access content.
Do not infer a candidate product’s privacy terms from a general architecture diagram. Microsoft’s governance guidance treats data access, processing, storage, retention, and compliance as organizational decisions; AWS describes role-based and least-privilege controls for knowledge-base services. Confirm how the actual product and contract handle your data (Microsoft guidance; AWS architecture guidance).
Test security at the model, tool, and network layers
Request the supplier’s threat model and test the controls against your own scenarios. Include malicious instructions in direct prompts, retrieved documents, and tool responses; attempted sensitive-data disclosure; unsafe or unauthorized tool calls; and unexpected outbound connections. Ask which protections are enforced centrally, what events are logged, how policy changes are managed, and what the response process is if an attack succeeds.
NIST’s concept paper explicitly raises direct and indirect prompt injection and minimizing impact after injection. Google Cloud documents policy-controlled gateways, content filters for prompt injection and sensitive-data leaks, and observability in its platform. Such documentation describes vendor-stated capabilities, not independent proof that controls will be effective in your environment. Validate them with realistic attack and failure cases (NIST concept paper; Google Cloud governance documentation).
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Make governance and operations part of the purchase
Determine whether your organization can keep track of agents after the initial deployment. A usable governance baseline should record each agent’s owner, purpose, environment, tools, access scope, version, and lifecycle state. It should also support access reviews, change management, activity monitoring, audit export, incident triage, and service shutdown.
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Check that logs contain enough detail for your audit and incident requirements, can be exported, and are retained for an appropriate period. If tamper resistance or verifiability is required, ask how the vendor provides it and test the evidence rather than assuming ordinary activity logs meet that standard.
Assign operational responsibilities across IT, security, data governance, legal, procurement, and the workflow team. Record who approves deployment, reviews access, responds to incidents, and owns a manual fallback. NIST recommends ongoing monitoring of third-party systems, incident plans, and tested fallback approaches; Microsoft recommends an organization-wide agent inventory and accountable ownership (NIST AI Profile; Microsoft governance guidance).
Check integration, deployment fit, and portability
Compare the candidate with your existing architecture: model access, tool execution, data retrieval, identity integration, network controls, deployment options, and observability. Test permission propagation through the actual connectors your workflow will use. Ask about supported APIs and protocols, versioning, rate limits, regional availability, upgrades, and interoperability with your monitoring and security systems.
Before signing, establish how you would leave. Ask whether you can export agents, prompts, policies, evaluation sets, logs, and organizational data; which components are proprietary; and what it would take to rebuild or move the workflow. AWS’s reference architecture separates model access, tools, knowledge bases, agents, and cross-layer security and observability, while Microsoft advises aligning standards and integration patterns with existing governance. Use those as architecture considerations, then verify the candidate’s actual capabilities (AWS architecture guidance; Microsoft governance guidance).
Run a comparable proof of concept
Have shortlisted vendors test the same representative task set under conditions that resemble the intended deployment. Include routine requests as well as ambiguity, access-denied cases, malicious retrieved content, unavailable tools, and recovery after failure. Agree on evaluation criteria before the vendor runs the tests.
Choose measures that match the workflow, such as task completion, correctness, harmful or unauthorized actions, human-escalation rate, latency, availability, reproducibility, and cost per completed workflow. Preserve traces for review and use human evaluation where outcomes cannot be scored mechanically. Record the dataset and prompt versions, model configuration, permissions, and test dates so results remain interpretable.
Treat vendor benchmark results as claims until you reproduce them under your conditions. The available buyer guidance does not establish a cross-vendor benchmark or universal passing score, so set thresholds based on the workflow’s risk and business requirements rather than borrowing a generic number (TechTarget buyer guidance; NIST AI Profile).
Estimate full operating cost and review supplier terms
Build a workload-based total-cost estimate rather than comparing license prices alone. Include platform licenses, model consumption, orchestration, tools and connectors, storage and retrieval, security and observability features, implementation, support, training, and expected human review. Ask how usage is measured, which limits apply, whether costs can be attributed to an agent or workflow, what budget alerts are available, and how costs change with volume or model choice. Microsoft specifically recommends per-agent or use-case cost tagging and budget alerts (Microsoft governance guidance).
Have procurement and counsel review content ownership and usage rights, subprocessors, audit rights, confidentiality, security duties, incident notification and response, service levels, product or model changes, liability, termination, data return and deletion, and business continuity. NIST recommends due diligence for intellectual property, privacy, security, and third-party dependencies, along with contract terms, continuous monitoring, incident response, and fallbacks (NIST AI Profile). The applicable obligations and acceptable risk depend on the workflow, jurisdiction, vendor offer, and negotiated contract.
Compare candidates using the same evidence
Use a shared scorecard for each candidate, with evidence drawn from demonstrations, configuration review, proof-of-concept traces, and contract documents—not feature claims alone.
| Evaluation area | Evidence to compare |
|---|---|
| Workflow fit | Completion on representative tasks and handling of exceptions. |
| Identity and authority | Distinct agent identity, least privilege, delegation, approvals, and revocation. |
| Data protection | Permission propagation, isolation, residency, retention, deletion, and secondary use. |
| Security | Prompt-injection and tool-abuse controls, outbound boundaries, and response process. |
| Governance and audit | Inventory, ownership, trace quality, policy enforcement, export, and intervention. |
| Integration and portability | Support for existing systems, deployment fit, standards, export, and migration route. |
| Reliability and support | Availability, recovery behavior, service levels, support response, and incident history. |
| Economics | Full workload cost, limits, usage attribution, budget controls, and scaling behavior. |
| Supplier and contract risk | Subprocessors, data and IP rights, auditability, change terms, liability, exit, and fallback. |
Weight the areas according to the workflow’s risk, your identity and cloud architecture, regulatory environment, and operational capacity. The evidence does not establish a universal best platform or a single acceptable risk threshold.
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