What Amazon Bedrock Agents are
Amazon Bedrock Agents are AWS's managed agents on top of Amazon Bedrock. The service handles orchestration: it interprets the user request, plans the steps, invokes external actions through action groups, and composes the answer using a foundation model hosted on Bedrock. It is the execution layer, running inside the company's AWS account.
What this service does not decide is the origin of the knowledge the agent uses to answer. An action group or a connected base that returns any document in the environment inherits all the clutter of the repository: drafts mixed with the official version, expired material, folders no one should read. Orchestration works well; what is missing is governing what enters as context.
So giving a Bedrock Agent access to internal data is less about connecting more sources and more about controlling which sources it can query, in what scope, and with what trace. That is where the governed-context layer comes in, kept separate from the agent's execution logic.
Why connecting Bedrock Agents to internal data needs governed context
An agent that answers fast but with no provable origin does not solve the problem of putting AI into production. The payoff appears when the answer comes from an approved source, within the right access boundary, and can be reconstructed later. Without that, the pilot impresses in the demo and stalls at scale, because no one trusts it to serve a customer or support a decision.
Contextfy organizes corporate knowledge into collections whose sources move from draft (DRAFT) to official (OFFICIAL) through approval. The Bedrock Agent queries only approved material, scoped by the credential of that collection. The support agent sees the service base; the contracts agent sees current policies and clauses; neither reaches what falls outside its own boundary.
The business effect is direct: less manual review rework, a shorter conversation with IT and compliance before going to production, and the capacity to put more agents into operation over the same trusted base. Governance here is not a brake; it is the mechanism that makes the agent trustworthy enough to leave the pilot and move into operation.
Risks of running Bedrock Agents without governing the context
When the agent queries raw sources directly, the problems do not show up in the demo; they show up in production, with a customer on the other end. The most common ones:
- Access too broad. An action group pointed at an entire drive or bucket lets the agent reach content that the use case should not see.
- Answer with no origin. With no approved source behind it, there is no way to prove which document and which version produced the answer when legal or audit asks.
- Outdated material. Drafts and expired versions in the same repository make the agent cite a rule that no longer holds.
- Hallucination from gaps. Without safe refusal, the agent fills the void with guesswork when the base does not cover the question.
- Context duplicated per runtime. Each agent rebuilds its own knowledge base, and answers diverge on the same topic.
Where Contextfy fits: architecture with Bedrock Agents
The Bedrock Agent keeps orchestrating inside Bedrock. Contextfy comes in before the answer, as the context source the agent queries: retrieval applies the collection scope, records the per-interaction trace, and returns only excerpts from approved sources. Execution stays in the agent; context governance lives in the dedicated layer, outside the action group logic.
There are two ways to consume it. Over REST, an action group calls the search (/v1/search) and ask (/v1/ask) endpoints and injects the result, already filtered and with sources, into the agent flow. Over MCP, the agent gains governed tools for context search and answer-with-sources. The two routes coexist; the choice depends on how much the team wants to delegate to the agent versus control in the action group.
Fontes
Drive, SharePoint, ERP, CRM, PDFs, APIs
Contextfy · Context Engine
Organiza · versiona · governa · observa o contexto
Runtimes
via MCP · API · conectores · pipelines
How to connect Bedrock Agents to Contextfy's governed context
In practice, consumption happens through open interfaces. On the REST path, you define an action group whose action calls /v1/search to retrieve approved excerpts or /v1/ask to receive a grounded answer with the sources that support it; the agent injects that return into its reasoning before replying to the user. On the MCP path, the agent gains governed context tools and decides when to invoke them during orchestration.
What makes consumption safe is credential scope. Each collection carries a set of approved sources, and the key used by the Bedrock Agent is restricted to the collections for that use case, with scope intersection applied at serving time. That boundary lives in the context layer, not in the action group, so switching the Bedrock model or the runtime later does not force you to rebuild the access rules.
A technical note for the record: there is no connector embedded inside Amazon Bedrock and no official integration endorsed by AWS. What is used are open interfaces, REST and MCP, compatible with API- and MCP-based architectures in general. The recommended path is a short pilot: expose one governed collection to a single agent, validate quality, refusals, and gaps with observability, and only then expand the scope.
Where Bedrock Agents with governed context already deliver value
The combination pays off most in operations where answering correctly, with a source, matters more than sounding clever:
Support and service
The agent answers tickets using only the approved base of policies and manuals, and refuses when the topic leaves the repository.
Sales and pre-sales
Technical answers and proposals built from authorized playbooks and material, with scope separated by product line.
Legal and contracts
Queries to the approved standard clause and the current policy, with a trace showing which document and version were used.
Internal operations
SOPs, manuals, and rules become queryable context inside the workflow, with access boundaries set per area.
Onboarding and enablement
The same source that trains the person feeds the agent, shortening the ramp for new hires.
Frequently asked questions
Does Contextfy replace Amazon Bedrock Agents?
No. Amazon Bedrock Agents is the AWS service that orchestrates and runs the agent on Bedrock; Contextfy is the layer that prepares and governs the context that agent queries. You keep Bedrock and gain control over the knowledge that feeds the answers.
How does the Bedrock Agent consume context from Contextfy?
Through open interfaces. Over REST, an action group calls /v1/search or /v1/ask and injects the result into the flow. Over MCP, the agent gains governed tools for context search and answer-with-sources. Scope and the per-interaction trace are applied before the excerpts are returned.
Are Bedrock Agents safe with internal company data?
Safety depends on how context reaches the agent. Pointed straight at an entire bucket, it inherits all the raw material. With Contextfy in between, it receives only approved sources within the credential scope, refuses on insufficient context when the base is missing, and every interaction is recorded with a traceId.
Can Bedrock Agents cite the source of an answer?
Yes, when context comes from a governed layer. Querying Contextfy over REST or MCP, the agent receives the answer along with the approved sources that support it and a trace identifier (traceId). If the question falls outside the approved base, it refuses instead of answering with no provable origin.
Do I need an official AWS connector to use Bedrock Agents with Contextfy?
No. The integration uses open interfaces, REST and MCP, compatible with API- and MCP-based architectures. There is no connector embedded in Amazon Bedrock and no official partnership: Contextfy is an independent governed-context layer, which avoids runtime or model lock-in.
Can I switch the Bedrock model or the runtime later?
Yes. The context base is independent of runtime and model. Source, scope, and permission rules live in the context layer, so changing the foundation model on Bedrock, or running another runtime in parallel, does not require rebuilding context governance.
Free diagnostic: we design the flow and the context scope for the pilot.
Assess how to give your Bedrock Agents governed context