Service 02
AI agents and RAG systems with visible boundaries
Build knowledge assistants and task agents that cite their sources, respect permissions, and know when to hand work back to a person.
Engagement fit
This service is worth examining when…
- Teams repeatedly search the same approved documents or knowledge sources.
- A task has clear inputs, permitted actions, and review points.
- The organization can define which sources are authoritative.
- Incorrect output can be detected, contained, and routed for review.
Example, not a result claim
A representative workflow with the review point left visible.
The actual design depends on your systems, data, permissions, operating rules, and the consequence of a wrong action.
TriggerA staff member asks a policy question
Step 1Confirm identity and source access
Step 2Retrieve relevant approved passages
Step 3Compose a bounded answer with citations
Step 4Check confidence and unsupported statements
Human checkAmbiguous or high-impact answers go to an owner
OutcomeThe user receives an answer, sources, and next step
What the work contains
Deliverables you can inspect and hand over.
Content inventory and retrieval design
Chunking, metadata, ranking, and citation approach
Prompt, tool, permission, and guardrail configuration
Evaluation set, failure review, monitoring, and handoff
Implementation approach
Controls that stay after the demo.
Reliability comes from decisions about retries, permissions, ownership, review, logging, and change.
- Write a narrow job description for the system before selecting a model.
- Separate retrieval quality, answer quality, and action safety in testing.
- Preserve source permissions instead of flattening private content into one index.
- Log enough context to investigate errors without retaining unnecessary personal data.
Typical integration context
Handoff
What your team receives.
- System and data-flow diagram
- Source refresh procedure
- Evaluation set and acceptance notes
- Prompt and tool change controls
- Escalation and incident runbook
Limits
What the service cannot honestly promise.
- A RAG system can still produce an unsupported answer; citations and review reduce risk but do not remove it.
- Outdated, conflicting, or poorly permissioned source material will weaken the system.
- Open-ended autonomous agents are not suitable for actions with material legal, financial, or safety impact.
- Model and provider behavior can change, so evaluation must continue after launch.
Measures
Observe the work, not the novelty.
Service questions
Useful answers before scoping.
What is RAG?
Retrieval-augmented generation lets a model answer using selected source material rather than relying only on what it learned during training. A useful RAG system also controls access, records sources, tests retrieval, and shows citations.
Can an agent update our business systems?
It can when the action is tightly defined and permissioned. We usually start with read-only access, then add low-risk actions, approval gates, limits, and logs. High-impact actions should remain subject to human authorization.
Do we need to train a model on our data?
Often no. Retrieval can use your approved content without training a new model. Fine-tuning may help with specific output patterns, but it is a separate decision from keeping answers grounded in current business knowledge.
Related solution areas
Bring us a real ai agents and rag systems with visible boundaries problem.
The audit request helps us understand the process, systems, data sensitivity, target window, and likely review needs before we recommend a next step.
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