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Evaluate an enterprise AI context platform on a real, permissioned workflow—not on a polished demonstration. First define the platform’s role in your architecture, then verify source coverage, retrieval quality, authorization, data flows, security evidence, resilience, portability, operating effort, and cost per successful task. Make legal, data-classification, security, identity, and deployment requirements pass/fail gates before scoring differentiators.
What an “AI context platform” includes
The label does not describe one consistent product category. A candidate may provide a retrieval layer that ingests enterprise content and returns relevant passages; a memory service for short- or long-term state; an agent runtime that calls tools; governance and evaluation controls; or a bundle of several layers.
AWS’s enterprise agent architecture separates model access, tools, knowledge bases, and memory. Knowledge bases can use vector or graph storage with semantic retrieval, while role-based controls enforce least privilege. Other vendors describe a broader platform that combines business context, execution, evaluation, governance, and auditing. Those are different scopes and should not be compared as if they were equivalent.
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Define the boundary before comparing products
For every candidate, write a one-page architecture statement answering:
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- Which systems ingest data, and which content types are supported?
- Does the product store indexes, embeddings, conversation memory, agent state, or all of them?
- Does it execute tools and workflows, or only supply context to another runtime?
- Which model endpoints can it call?
- Where are authorization, approvals, policy enforcement, evaluation, and audit logging performed?
- Which components remain your responsibility?
Do not award points for a feature that is outside the product’s actual boundary. A retrieval service, an agent runtime, and a fully managed application platform have different risks, costs, and exit paths.
Start with a bounded workflow and a baseline
Choose one business process with a named owner, identifiable users, representative source systems, and a measurable cost or quality baseline. AWS recommends beginning with a well-defined, limited-scope project and documenting success metrics before scaling.
Write the workflow contract
Describe the task in operational terms rather than “improve productivity.” For example, an internal support agent might locate the current procedure, determine whether the requester is authorized, draft an answer with source references, and route an exception to a human. Record the current handling time, error rate, escalation rate, and review effort so a pilot can be compared with the existing process.
Specify what completion means and what must never happen. A successful run might require a correct answer, a current source citation, and a complete audit record. Prohibited outcomes might include exposing another employee’s case, sending an unapproved external message, or changing a record without authorization.
Set decision rights and human handoffs
- Name the person accountable for business acceptance.
- List actions that are read-only, actions that require approval, and actions the system must never perform.
- Define when a human must take over: missing evidence, conflicting records, low confidence, policy uncertainty, or a failed dependency.
- Document the user and role profiles that the pilot must represent.
Without these boundaries, a vendor can optimize a demo while the buyer has no defensible way to judge risk or value.
Verify source coverage, retrieval, and authorization
Build an inventory of every source the workflow needs. Include structured databases, document repositories, ticketing systems, wikis, file shares, email or chat archives where applicable, and the identity providers that govern access. Record content types, owners, update frequency, deletion rules, versioning, and the permission model for each source.
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Test retrieval with current and difficult data
Use a held-out test set containing routine questions and edge cases: duplicate documents, conflicting versions, tables, scanned files, acronyms, recently changed procedures, deleted records, and deliberately irrelevant material. Measure whether the system returns the right evidence, not merely whether it produces fluent text.
- Relevance: Are the retrieved passages directly useful for the task?
- Freshness: Does a changed or withdrawn source stop influencing answers within the interval your workflow requires?
- Grounding: Does the response accurately reflect the retrieved material?
- Source traceability: Can a reviewer open the document, section, record, or revision used?
- Deletion behavior: After a source is deleted or access is removed, does it disappear from retrieval and cached representations?
There is no universal freshness threshold. A safety procedure, a financial limit, and a low-risk knowledge article may require different update windows; set the threshold from the workflow’s consequences.
Prove permission fidelity
Run the same query under multiple identities and roles. Include a user who should see the record, one who should not, a user whose access was recently revoked, and a service identity with delegated tool permissions. Verify both retrieval and generation: a hidden document must not leak through a summary, citation, metadata field, cached answer, or conversation memory.
Ask vendors to demonstrate how permissions are synchronized, how group changes propagate, and how stale identity data is detected. Test least-privilege defaults and confirm whether write actions use the end user’s identity, a service account, or a delegated authority that requires approval.
Map every data flow and deployment boundary
Request a data-flow diagram for the exact architecture and configuration you would buy. A generic reference diagram is not sufficient. Trace each data class from ingestion through indexing, retrieval, model calls, tool execution, logging, support, backup, export, and deletion.
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|---|---|
| Prompts and user messages | Where are they processed, retained, encrypted, and exposed to support personnel? |
| Retrieved content | Is it sent to a model provider, cached, logged, or included in traces? |
| Indexes and embeddings | What region stores them, who can query them, and how are deletions propagated? |
| Tool payloads and write results | Which systems receive them, under which identity, and with what approval control? |
| Run traces, recordings, and telemetry | What fields are captured, how long are they kept, and can customers export or delete them? |
| Backups and support copies | Where do they reside, who can access them, and what is the destruction schedule? |
For every flow, establish location, residency, provider egress, access, retention, deletion, encryption, and contractual handling. Endpoint-level retention and training settings can depend on the selected architecture; obtain the terms for the specific model endpoint rather than relying on a product-wide statement.
Rank #3
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Compare deployment patterns by responsibility
| Pattern | Evaluate |
|---|---|
| Managed service | Provider-operated boundaries, available regions and endpoints, support access, update cadence, and recurring operating cost. |
| Customer cloud account or virtual network | Which components remain provider-managed, private connectivity options, patching duties, and cross-account data movement. |
| On-premises | Hardware capacity, model and connector availability, upgrade process, local support, and total administration effort. |
| Disconnected operation | Offline update path, security-feed delivery, model refreshes, dependency handling, and latency under local capacity. |
Some vendor documentation describes managed, customer-controlled, on-premises, or disconnected patterns and may state that the service is not designed for Sensitive Data. Treat that as a scope limitation to verify against your classification policy, not as a substitute for your own data review.
Require security and governance evidence
Security claims are hypotheses until you inspect evidence. Ask for artifacts covering the complete system, including agents and tools, rather than only the hosting layer.
Identity and authority
- Tenant isolation and administrative separation.
- Single sign-on, automated provisioning and deprovisioning, and support for your identity provider.
- Workforce identities, agent identities, service accounts, and delegated authority.
- Least-privilege roles for retrieval, tools, administration, and support.
- Secrets storage, rotation, customer-managed keys where required, and emergency revocation.
- Approval gates for external communication, transactions, and destructive actions.
Threat and policy controls
- Input threat handling, prompt-injection resistance, and instruction hierarchy.
- Output filtering and detection of sensitive-data exfiltration.
- Tool-level policy enforcement and parameter validation.
- Rate limits, sandboxing, network egress controls, and safe failure modes.
- Policy versioning, change approval, testing, and rollback.
Audit and incident evidence
Confirm that logs capture the user, agent, model and version, retrieved sources, tool calls and parameters, approvals, policy decisions, errors, and final output. Determine whether timestamps are reliable, whether logs are tamper-evident, how long they are retained, and whether they can be exported to your security monitoring system.
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Design a representative, adversarial pilot
Give every bidder the same task set, data conditions, user profiles, pass thresholds, and reporting format. Keep a held-out set that vendors cannot tune against, and preserve raw results so procurement can compare like with like.
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Build the test set
- Select real, permissioned tasks from the chosen workflow and remove unnecessary personal or confidential data.
- Label expected outcomes, required sources, prohibited disclosures, allowed tools, and approval points.
- Include normal cases, ambiguous requests, stale or missing sources, conflicting records, revoked access, prompt injection, malformed inputs, and dependency failures.
- Run the same cases with representative users and permission profiles.
- Have business owners, security reviewers, and operations staff score results independently.
Measure task quality and grounding
- Business correctness and completion rate.
- Groundedness: whether claims are supported by retrieved evidence.
- Source quality, citation accuracy, and ability to reproduce the answer.
- Appropriate abstention and escalation when evidence is inadequate.
- Unauthorized disclosure, prompt-injection success, unsafe tool calls, and approval bypasses.
Do not collapse these into one “accuracy” number. A system that answers more questions by guessing may look productive while increasing operational risk.
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Measure resilience and operations
- Behavior when a source is unavailable, stale, or contradictory.
- Provider outage, rate-limit, timeout, and partial-tool failure behavior.
- Latency distributions, throughput, error rate, retry rate, and queue behavior.
- Cost per completed task, including retries, retrieval, model calls, storage, and human review.
- Connector setup and maintenance time, monitoring effort, upgrade work, and time to investigate a bad run.
AWS identifies latency, throughput, error rates, and cost per inference as useful operational measures. Those are measurements to collect, not universal acceptance thresholds; set thresholds from your baseline and service objectives.
Test portability and operating fit
Ask each vendor to demonstrate an exit, not merely describe one. Specify what can be exported in usable form:
- Source and identity configuration.
- Indexes, embeddings, context, and memory.
- Evaluation datasets, labels, traces, and audit history.
- Policies, prompts, agent definitions, tool schemas, and approval rules.
- Connector mappings and transformation logic.
Check API access, supported interfaces, export formats, documentation, and migration tooling. Estimate the work to move to another model or retrieval provider. Identify what breaks if you change a model endpoint, vector store, identity provider, or cloud region.
Portability also includes operating ownership. Assign responsibility for connector maintenance, monitoring, upgrades, incident response, user support, policy changes, and recovery testing. A technically exportable system can still be expensive to leave if your team must recreate undocumented workflows or manually rebuild permissions.
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Compare total cost for a successful workflow
Model the cost of a completed, accepted task rather than a seat, token, or inference headline. Include:
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- Platform and environment fees.
- Model inference, embeddings, retrieval, and storage.
- Network transfer and provider egress.
- Implementation, connector development, and ongoing connector upkeep.
- Evaluation, red-teaming, monitoring, security reviews, and compliance work.
- Human approval, exception handling, and support.
- Operations, upgrades, backups, disaster recovery, and migration planning.
Ask vendors to price the same workload and state assumptions: task volume, average and maximum context, model mix, retries, storage growth, support tier, minimum commitment, overages, renewal or indexation terms, and termination assistance. Use the pilot’s measured completion rate to calculate cost per successful task. A lower unit price is not cheaper if it produces more escalations, rework, or security review.
Use gates, then a weighted scorecard
Apply pass/fail gates before assigning points. Typical gates include legal approval, data classification, required residency, security controls, identity integration, contractual audit rights, and the required deployment boundary. A candidate that fails a gate should not win through strengths in unrelated categories.
| Axis | Evidence to collect |
|---|---|
| Workflow fit | Representative task results against a documented baseline and business acceptance criteria. |
| Sources and retrieval | Connector coverage, update and deletion behavior, relevance, freshness, and citations. |
| Authorization | Per-user retrieval tests, revocation results, least privilege, delegated authority, and write permissions. |
| Data boundaries | Exact data-flow diagram, residency, egress, retention, encryption, subprocessors, and contract terms. |
| Security and governance | Audit artifacts, identity controls, policy enforcement, approvals, vulnerability handling, and incident evidence. |
| Quality and resilience | Held-out pilot results, adversarial tests, degraded-mode behavior, latency, throughput, and error rates. |
| Observability | Trace detail, source and action history, export capability, retention, and investigation workflow. |
| Deployment and operations | Hosting choices, endpoint options, update path, support model, recovery, and clear customer/vendor duties. |
| Interoperability | APIs, exportability, model and provider flexibility, supported interfaces, and migration plan. |
| Economics | Cost per successful task plus full lifecycle operating and exit costs. |
Set weights to reflect the workflow. A regulated write-enabled process may weight authorization, approvals, auditability, and resilience more heavily than interface polish. Record the evidence behind every score and mark claims that remain unverified.
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- Which product components are included, and which model, vector, graph, memory, tool, or monitoring services are separate?
- Show the exact architecture and every data flow for our proposed deployment.
- Which endpoints receive prompts, retrieved content, embeddings, tool payloads, traces, backups, or support copies?
- What are the retention, deletion, training-use, residency, and subprocessor terms for each endpoint?
- How are source permissions synchronized, tested, revoked, and audited?
- How do agent and service identities differ from human identities?
- Which actions require approval, and can policies prevent tool calls before execution?
- What happens during stale data, missing sources, model outage, rate limiting, or connector failure?
- What evidence covers tenant isolation, penetration testing, vulnerabilities, incidents, and audit reports?
- What can we export, in which formats, and what assistance is included at termination?
- Which tasks, usage assumptions, support levels, minimums, overages, and renewal terms determine the quoted cost?
- Will you run the agreed pilot, disclose raw results, and accept independent testing?
Make the purchase decision defensible
Keep the workflow definition, baseline, test cases, thresholds, architecture diagram, security artifacts, cost model, pilot outputs, and unresolved exceptions in one decision record. Require sign-off from the business owner, security, data protection, architecture, operations, and procurement functions. If a material claim cannot be verified, score it as unproven rather than assuming it is true.
AWS Prescriptive Guidance captures the governing principle: “Security and governance cannot be afterthoughts in generative AI implementation.” Treat context as a system of data, identities, models, tools, policies, and operations. The right platform is the one that meets your non-negotiable controls and demonstrably completes the target work with acceptable quality, resilience, latency, cost, and operational burden—not the one with the broadest feature list.
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