The Tool Desk
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What grounding can—and cannot—do
Grounding connects model output to verifiable information. Google Cloud describes it as a way to tether generated output to provided data, reduce the chance of invented content, and make answers auditable through source links. Depending on where trusted information lives, documented options include enterprise document RAG, managed RAG, existing Elasticsearch indexes, search APIs, and web search.
Those options are not interchangeable guarantees of accuracy. Choose a source based on its authority for the question, how quickly it changes, and the access and compliance controls it needs. A policy answer may belong in an approved document collection; a current account entitlement or order status may require a live system or API rather than a static document. AWS agentic-AI guidance likewise emphasizes making retrieval and intermediate stages observable.
The key limitation is that retrieval supplies evidence, not truth. A retrieved page can be stale, incorrectly ranked, or maliciously changed. An agent can cite a source and still give a confidently wrong answer if that source is wrong or the system has been manipulated.
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Start with one bounded workflow
Choose a task with an owner and a testable outcome
Begin with a recurring job that has a clear business owner, an identifiable source of truth, measurable success criteria, and limited permitted actions. Microsoft’s AI Agent Adoption Guidance gives information retrieval and synthesis, ticket creation, and system monitoring as examples of agent workflows. These examples are starting points, not proof that any particular deployment will be safe or accurate.
Write down what the agent is authorized to do and what falls outside its scope. Define whether an uncertain or out-of-scope request should trigger a clarifying question, a refusal, or a human handoff. Keep read-only answers separate from write actions until the read path has been validated.
Separate model judgment from hard rules
Use the model for language understanding and bounded ambiguity; use deterministic application logic for hard policy rules and state changes where possible. Give the agent only the collections and tools needed for the task. Narrow tool permissions and schemas to the required operation, and put confirmation or human review in front of consequential actions.
For a multi-stage job, make the stages explicit, with typed inputs and outputs and a defined failure path for each. AWS guidance recommends modular stages and fallbacks so that a failure in one component is less likely to become an opaque end-to-end failure.
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Choose and govern the source-of-truth layer
Map sources to the facts they can authoritatively answer
Inventory the sources the workflow needs, who owns them, how often they change, and how permissions, updates, versions, and deletions reach retrieval. Prefer references that can be shown to the user and retained for audit. For frequently changing facts—such as account state, entitlements, or order status—use an appropriately authorized current system or API when available instead of assuming a document snapshot is current.
Google Cloud’s grounding overview documents several ways to connect a model to information. The practical choice depends on the data’s location and authority, required freshness, and governance needs; a feature label alone does not establish that a source is suitable.
Treat retrieved text as evidence, not policy
Retrieved documents must not be allowed to override the agent’s system policy or tool permissions. Validate source ownership and ingestion paths, preserve source and index version history, and restrict who can change source content or indexes. Microsoft’s Grounding Data Compromise guidance describes risks involving public pages, internal wikis, vector databases, embeddings, and index metadata. AWS responsible-AI guidance also treats factual accuracy as a system concern, not something solved just by adding context.
Monitor which sources are retrieved, whether their ranking or distribution changes unexpectedly, and whether new or low-trust material begins appearing repeatedly. Review or scan newly added low-trust content before relying on it for consequential answers.
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Make answers evidence-aware
For factual responses, instruct the agent to use retrieved evidence and attach source references or links where the product supports them. Distinguish what a source states directly from the agent’s inference. A useful response contract should specify:
- Which claims require source support.
- How to present the supporting source so a user can verify it.
- What to say when the sources are missing, conflicting, or too old to establish the answer.
- When to ask for clarification, decline, or route the request to a person.
- Which actions are prohibited without approval, even if a retrieved document suggests otherwise.
If evidence does not establish an answer, the agent should say what it could not verify and give a safe next step rather than fill the gap with a plausible-sounding guess. These are implementation recommendations derived from vendor grounding and evaluation guidance, not a universally validated prompt formula.
Test retrieval, answers, and actions separately
Build a repeatable regression set
Create realistic cases for ordinary requests as well as ambiguous inputs, out-of-scope requests, stale or conflicting documents, permission boundaries, and tool failures. Use grounded test data and assertions that can be checked—for example, a required value is correct, the right policy source is cited, unauthorized information is absent, or a write tool is not called without approval.
Score retrieval quality separately from answer groundedness and task completion. This helps locate whether a failure came from retrieving the wrong material, reasoning incorrectly from good material, or taking an unauthorized or unsuccessful action. Keep known failures as regression cases and run the same set before deploying changes to prompts, models, tools, or knowledge sources. Microsoft’s Agent evaluation overview recommends specific scenarios and verifiable criteria; it cautions that without evaluation, teams cannot reliably tell whether a change improved or degraded quality.
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Judge successful completion by outcome, not uptime
A run can finish without a runtime error and still produce a wrong, incomplete, or policy-breaking result. AWS documentation on agent evaluation therefore supports evaluating response and task quality, not just whether the system stayed online. Define pass criteria around the actual business task and its safety boundaries, not merely whether the agent returned text.
Keep production feedback in the loop
Sample production traces, score them against the same quality concerns, and feed incidents into the regression set. Version evaluation cases alongside the agent configuration so that results can be compared across changes. AWS CloudWatch agent-evaluation documentation describes a development loop of curating traces, scoring them with evaluators, and comparing versions, as well as online evaluation of sampled live traffic.
Capture enough trace information to attribute problems: user input, retrieved context or references, tool calls, outputs, and evaluation signals. Monitor per-stage health and retrieval quality as well as end-to-end results. The goal is not simply to collect logs, but to determine whether a failure began in retrieval, answer generation, or action execution.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare implementation choices on operational fit
Evaluate grounding and agent approaches against the workflow’s requirements rather than assuming one implementation is universally best. The vendor documentation reviewed here provides implementation guidance, not independent head-to-head measurements or a universal hallucination-reduction percentage.
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| Decision area | Questions to answer |
|---|---|
| Source authority and freshness | Is the source authoritative for this fact? Is it an enterprise corpus, live API, or public web source, and who owns and updates it? |
| Retrieval quality | Does retrieval find and rank the right material, respect permissions, handle useful chunking, and behave safely when there is no result? |
| Traceability | Can users see source attribution, and can operators inspect or replay the relevant retrieval and tool steps? |
| Workflow control | Are permissions and tool allowlists narrow? Are human approval, deterministic checks, and fallback behavior defined? |
| Evaluation and observability | Can the team use task-specific grounded test data, compare regressions, sample production, and monitor the stages that can fail? |
| Operational and governance fit | Do latency, cost, access controls, retention, regional requirements, and platform compatibility fit the deployment? |
Do not report a universal improvement percentage based on an illustrative metric or vendor guidance. Establish your own baseline and state the task, dataset, model and configuration, date, and evaluation method when reporting results. The reviewed documentation does not establish a universally best model, retrieval configuration, or effect size.
A practical deployment checklist
- Bound the job: name the owner, source of truth, measurable outcome, authorized actions, and refusal or escalation conditions.
- Prepare the evidence: inventory authoritative sources, owners, freshness, permissions, versioning, and deletion behavior; select retrieval or live system access to match the facts required.
- Limit authority: expose only task-relevant sources and minimum necessary tools; keep hard rules deterministic and add approval to consequential writes.
- Specify the answer contract: require evidence for factual claims, make sources inspectable, and define behavior for missing, conflicting, stale, or out-of-scope evidence.
- Test in stages: assess retrieval, answer support, policy boundaries, and task completion with repeatable cases before deployment.
- Secure and monitor: control source and index changes, inspect traces and top retrieved results, test adversarial inputs, and turn production failures into regression cases.
This approach reduces opportunities for unsupported answers by narrowing what the agent can know and do, while making failures easier to find. It does not eliminate the need to verify the source material or to review performance against the organization’s own users, corpus, and controls.
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