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Evaluate an enterprise AI context platform against a real, permissioned workflow—not a polished demonstration. First define the product’s actual boundary, then make legal, data, security, identity and deployment requirements pass/fail gates. For candidates that pass, run a controlled pilot measuring task quality, retrieval and authorization, adversarial safety, resilience, latency, cost per successful task, portability and operating effort.
Vendor feature pages describe intended capabilities, not proof that your controls or workloads will work. Require architecture diagrams, current security artifacts, contractual commitments and reproducible pilot results before approving a purchase.
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Define what “AI context platform” means in your shortlist
“AI context platform” is not a consistent product category. One supplier may provide ingestion, indexing and retrieval; another may add long-term memory, an agent runtime, tool execution, evaluation and governance. A third may bundle those layers with model access and application-building features. Comparing these products as if they were interchangeable creates false gaps and inflated feature checklists.
A useful starting model separates the components an enterprise agent needs:
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| Layer | What it may provide | Questions for the vendor |
|---|---|---|
| Model access | Connections to one or more language or embedding models, routing and inference controls. | Which models and endpoints are supported? Can you change providers without rebuilding indexes, prompts or policies? |
| Knowledge and retrieval | Connectors, parsing, indexing, vector or graph storage and semantic retrieval. | Which source systems and content types are supported? How are updates, deletions, citations and access controls represented? |
| Memory and context | Short-term conversation state, long-term memory, runbooks or institutional context. | What is stored, for how long, under whose authority, and how can a user or administrator inspect, correct or delete it? |
| Tools and agent execution | Actions against business systems, delegated authority, workflow orchestration and approvals. | Can permissions be enforced per tool and per user? Which actions require human approval, and what happens when an approval is denied? |
| Evaluation and governance | Test sets, policy enforcement, tracing, audit, monitoring and incident workflows. | Which events are logged, exported and retained? Can security and compliance teams investigate a complete run? |
| Application bundle | A broader environment combining context, execution, governance and user-facing applications. | Which capabilities are native, which are partner services, and which require separate contracts or infrastructure? |
Architecture guidance from AWS distinguishes model access, tools and knowledge bases, with role-based access enforcing least privilege. OpenAI’s Frontier page illustrates a broader vendor-defined offer that combines business context, execution, evaluation, governance and auditing. Treat both descriptions as useful architectural vocabulary, not as evidence that a particular product meets your requirements.
Start with one measurable, permissioned workflow
Choose a bounded task with a named business owner, identifiable users, representative source systems and a measurable cost or quality baseline. A good first workflow has enough business value to justify instrumentation but is narrow enough that you can define correct and unacceptable behavior.
Write the workflow contract
- Purpose: State the business decision or action the system supports.
- Users: List the roles, identity providers and permission profiles that must be represented.
- Sources: Name the systems, repositories, records and content types the task requires.
- Success: Define a correct answer or completed action, including required citations, fields or approvals.
- Prohibited behavior: Specify data that must never be disclosed, actions that must never be taken automatically and situations that require escalation.
- Human control: Identify the approver, takeover point and evidence the reviewer needs.
- Baseline: Record current handling time, error rate, rework, volume and cost using the same task definition you will use in the pilot.
A baseline prevents a demo from becoming the comparison standard. AWS recommends limited-scope projects with documented success metrics before scaling. Do not begin with an abstract goal such as “make employees more productive”; begin with a task that can be sampled, scored and reproduced.
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Set acceptance criteria before inviting bids
Document which outcomes are mandatory and which are differentiators. For example, a candidate may be disqualified if it cannot enforce per-user access to a required repository, operate inside a mandated cloud boundary or provide an audit trail for tool calls. Retrieval speed, interface polish or additional connectors should not compensate for a failed control.
Apply pass/fail gates before weighted scoring
Use gates for requirements that cannot be traded away. Obtain written confirmation and supporting evidence; do not award a passing result solely because a salesperson says a feature is “on the roadmap.” Typical gates include:
- Legal approval for the intended data classes, jurisdictions and processing purposes.
- Identity integration, including single sign-on, provisioning or deprovisioning and the required user and service-account model.
- Per-user authorization for retrieval and actions, with revocation taking effect within the period your workflow permits.
- A deployment boundary that matches policy: managed service, customer cloud account or virtual private cloud, on-premises, or disconnected operation.
- Contractual restrictions on provider access, training use, retention, deletion, subprocessors, breach notice and data return.
- Security evidence acceptable to your risk function, including the scope and date of independent audits or tests.
- Required integration interfaces, export formats and support commitments.
A failed gate should stop the evaluation or trigger a documented exception process. Do not hide a mandatory deficiency inside an average score.
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Test source coverage, retrieval quality and authorization
Build a source inventory
For every required system, record the connector, object types, metadata, update mechanism, identity source, permission model, deletion process and expected change frequency. Include documents, tickets, databases, file shares, chat or collaboration records and structured business data where the workflow depends on them. Identify records that contain customer information, personal data, regulated material or secrets.
Ask whether the platform distinguishes institutional material such as runbooks from customer content and personal data. Context’s documentation makes that distinction; it also says its service is not designed for Sensitive Data. That is a vendor-stated scope limitation to verify against your own classification policy, not a general definition of what the product can safely process.
Measure retrieval, not connector count
A connector that imports data but returns the wrong passage is not coverage. Use held-out questions and tasks that include synonyms, ambiguous terms, recent updates, conflicting documents and requests requiring several sources. Score whether the returned material is relevant, complete enough for the task and accompanied by a source reference when one is required.
Set freshness from the workflow’s risk, not from a generic promise. A daily update may be adequate for a historical policy search but unacceptable for inventory or incident response. Test an edit, an addition and a deletion, then record the time until each change affects retrieval. Do not infer a universal freshness threshold from vendor examples.
Exercise permission boundaries
Run identical queries as users with different roles. Include a user who should see a record, one who should not, a service account with narrower privileges and an administrator whose access is intentionally different. Revoke access during the test and verify that both retrieval and any generated citation stop exposing the item. Check whether snippets, embeddings, caches, conversation memory, logs and exports retain data after the source permission changes.
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Test indirect disclosure. A system can refuse to display a document while still revealing its title, existence, sensitive attributes or a summary learned earlier. Include requests that ask the agent to compare restricted and unrestricted records, infer a hidden value or use a tool to retrieve the item on the user’s behalf.
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Map every data flow and deployment boundary
Require a data-flow diagram for the exact architecture and configuration under consideration. A generic reference diagram is not enough. Trace each class of data through ingestion, indexing, retrieval, model calls, tools, monitoring and support.
Data classes to trace
- User prompts and conversation history.
- Retrieved content, citations and metadata.
- Indexes, embeddings, graph records and memory.
- Model requests and responses, including retries and fallback models.
- Tool arguments, returned records and approval payloads.
- Run traces, recordings, telemetry, error reports and debug artifacts.
- Support-session access, backups, disaster-recovery copies and administrative exports.
- Deletion requests, retention expiry and migration exports.
For each class, establish physical or logical location, people and services with access, encryption, retention, deletion behavior, provider egress, subprocessors and contractual treatment. Ask whether data can cross regions during support, failover, model routing or abuse monitoring. Require the answer for production, backup and test environments.
Compare deployment patterns by responsibility
| Pattern | Boundary questions | Operating questions |
|---|---|---|
| Managed service | Which provider systems receive prompts, retrieved data, telemetry and backups? What endpoint-level retention and training settings apply? | Who patches, scales and monitors the service? Which logs and configuration can your team export? |
| Customer cloud account or private network | Which components remain outside your account? What network paths, private endpoints and egress controls are required? | Which upgrades, connectors and incident tasks become your responsibility? |
| On-premises | Can all required model, identity and tool endpoints operate inside the permitted boundary? | Who supplies hardware capacity, security updates, model updates and support? |
| Disconnected operation | How are models, connectors, policies and security fixes transferred without live external access? | What is the update cadence, approval process and latency impact of the isolated environment? |
Context describes managed, customer-environment, on-premises and disconnected patterns, but its endpoint-level provider retention and training settings depend on architecture. Verify those settings in the chosen configuration and contract rather than generalizing from a product page.
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Security review should cover the complete system: data ingestion, retrieval, models, agents, tools, identities, outputs and operations. Request artifacts with scope, dates, exceptions and remediation status.
Identity, authority and secrets
- Tenant isolation design and test evidence.
- Workforce identity, service identity and agent identity, including SSO and automated provisioning.
- Least-privilege roles and separation between reading, writing, approving and administering.
- Delegated authority rules for agents acting for a person or team.
- Secrets storage, rotation, masking and prevention of secrets in prompts or traces.
- Tool-level permissions, allowlists, rate limits and approval gates for consequential actions.
Audit, assurance and response
- Audit-log fields for user, agent, source, retrieved content, policy decision, tool call, approval and result.
- Retention, immutability, export and integration with your investigation tooling.
- Vulnerability disclosure, patch timelines, penetration-test reports and unresolved findings.
- Incident-response procedures, customer notification obligations and evidence preservation.
- Independent audit reports, such as SOC 2 or ISO 27001, with the applicable scope and period.
Context says SOC 2 and ISO 27001 reports are available on request. Review the actual reports, boundaries, control exceptions and period; a certification name without scope does not establish coverage of your deployment.
AWS Prescriptive Guidance states, “Security and governance cannot be afterthoughts in generative AI implementation.” Its related guidance also notes that “Observability, security and discoverability span multiple layers, ensuring that AI operations are monitored, auditable, and compliant with enterprise policies.” Treat those statements as design principles: your review should show how each layer is monitored and governed in practice.
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Run a representative and adversarial pilot
Construct the test set
Use real, permissioned tasks with identifying details removed or otherwise handled under your approved data process. Include representative users, source distributions, difficult cases and expected outcomes. Keep a held-out portion that vendors cannot tune against. Publish the task definitions, scoring rubric and pass thresholds to every bidder so results are comparable.
Record the baseline for each task before enabling the platform. A reviewer should be able to reproduce the score from the input, retrieved sources, output, tool events and approval history.
Include failure and attack scenarios
- Prompt injection: Place conflicting instructions in a retrieved document and test whether the agent treats untrusted content as data rather than authority.
- Unauthorized disclosure: Ask users to retrieve, summarize, infer or compare records outside their permissions.
- Unsafe tool use: Present requests that would create, delete, transfer or publish data without the required approval.
- Stale or missing sources: Remove or age a required record and check whether the system signals uncertainty instead of inventing an answer.
- Revocation: Change a source permission during a session and inspect retrieval, memory, citations, caches and exports.
- Dependency failure: Simulate model outage, connector failure, rate limiting, network interruption and partial tool responses.
- Degraded operation: Observe whether the system fails closed, falls back safely or silently lowers controls.
Measure the outcomes
| Measure | What to capture |
|---|---|
| Business correctness | Task completion, factual accuracy, required fields, groundedness and source quality. |
| Retrieval behavior | Relevance, completeness, freshness, citation accuracy and permission fidelity. |
| Safety | Unauthorized disclosure, injection resistance, unsafe actions and approval compliance. |
| Resilience | Behavior under missing sources, provider outage, rate limits, retries and degraded dependencies. |
| Performance | Latency, throughput, error rate and tail behavior for the same workload. |
| Economics | Inference, retrieval, storage, network and human-review cost per completed task, including retries. |
| Operations | Connector setup, maintenance, monitoring, policy changes and time to investigate a bad run. |
AWS identifies latency, throughput, error rates and cost per inference as useful operational measures. Convert them to your workflow’s units, especially cost per successfully completed task. There is no established market-wide performance statistic or universal threshold for this buying decision; publish your own acceptance criteria instead of quoting a vendor survey as a benchmark.
Make evidence independently checkable
Preserve inputs, expected outcomes, system configuration, model and retrieval versions, logs and reviewer decisions. Have security or internal audit inspect high-risk cases. F5-hosted guidance describing NSS Labs research recommends measurable evidence and independent validation across input threats, output leakage, resilience, policy governance, tool invocation, observability and interoperability. Its conclusion is direct: “Vendors who resist meaningful testing signal immaturity, not strength.” That is buyer guidance, not a regulatory finding, but refusal to permit reasonable testing is a material procurement signal.
Evaluate portability and operating fit
Ask what you can export in a usable format: source and identity configuration, indexes, context or memory, evaluation data, traces, policies and agent or tool definitions. Confirm whether exports include permissions and metadata or only raw content. Test API access rather than accepting a statement that an API exists.
Model a provider change. What breaks if you replace the language model, embedding model, retrieval engine or tool endpoint? Estimate the work to rebuild indexes, revalidate prompts, migrate policies, re-permission data and rerun the evaluation set. Portability is not merely file export; it is the ability to reproduce an approved workflow elsewhere.
Assign operational ownership explicitly. For each connector, upgrade, policy change, alert, incident, capacity increase and user-support request, name the responsible party and required response time. A managed service may reduce infrastructure work while leaving your team responsible for source permissions, evaluation and business approvals.
Compare total cost and commercial terms
Price the expected completed workflow, not only seats or model tokens. Build a lifecycle model that includes:
- Platform or environment fees.
- Model inference, embeddings and retrieval.
- Index, memory, trace and backup storage.
- Network transfer and provider egress.
- Implementation, integration and connector maintenance.
- Evaluation, red-team testing and security review.
- Human approvals, exception handling and support.
- Monitoring, upgrades, incident response and internal operations.
- Exit, export and migration work.
Give every vendor the same workload, volume, retention, latency and support assumptions. Request minimum commitments, overage rates, support tiers, renewal or indexation terms, service-level remedies and termination assistance in writing. Report both cost per successful task and annual operating cost; a low inference price can be outweighed by review, connector or migration effort.
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After gates are passed, choose weights based on the workflow’s risk and economics. A simple internal scale such as zero for no evidence, one for a claim only, three for documented and demonstrated capability, and five for independently verified results can make confidence visible. Do not average a failed mandatory control into an acceptable total.
| Axis | Evidence to collect |
|---|---|
| Workflow fit | Results on representative tasks against the documented baseline. |
| Sources and retrieval | Connector coverage, update and deletion behavior, relevance, freshness and citations. |
| Authorization | Per-user access tests, revocation results, least privilege and write permissions. |
| Data boundaries | Complete flow map, residency, egress, retention, deletion and contract terms. |
| Security and governance | Audit artifacts, identity, delegated authority, approvals, vulnerability and incident evidence. |
| Quality and resilience | Held-out pilot results, adversarial tests and degraded-mode behavior. |
| Observability | Trace detail, source and action history, export and investigation workflow. |
| Deployment and operations | Hosting choices, endpoints, upgrades, support model and customer/vendor duties. |
| Interoperability | APIs, exportability, model and provider flexibility and migration effort. |
| Economics | Cost per successful task and full lifecycle operating cost under shared assumptions. |
Keep the test set, configuration, scores and exceptions with the procurement record. Re-run critical tests after material changes to models, connectors, policies or deployment.
Make the buying decision and contract the controls
Shortlist only candidates that pass the mandatory gates and produce reproducible pilot evidence. Select the platform whose architecture fits your boundary and whose measured task value justifies its full operating burden—not the one with the longest feature list.
Before signature, turn successful pilot conditions into obligations: approved data uses, retention and deletion, subprocessor disclosure, incident notice, audit rights, identity and authorization behavior, service levels, change notification, export formats, termination assistance and limits on provider access or training use. Attach the tested architecture and acceptance criteria so a later configuration change cannot silently invalidate the assessment.
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