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Operationalize context by treating it as a governed information and execution system—not as a longer prompt. Define what the agent may know and do, assemble authoritative data, live evidence, instructions, tools, and memory with their sources and permissions intact, then evaluate and monitor the behavior that results.
What counts as context for an enterprise AI agent?
Context is the information state made available to a model during inference. It includes more than the prompt: instructions, tool definitions, retrieved data, runtime evidence, conversation state, and memory all affect what the agent can respond to or do. The useful context changes as a task unfolds, so it must be assembled and managed repeatedly.
Anthropic describes context engineering as curating and maintaining the optimal information set during inference, including information beyond prompts, in its article Effective context engineering for AI agents (published September 29, 2025). This makes context engineering an operational discipline: the system must decide what to provide, when to provide it, whose information it is, and how to keep it current.
Keep three kinds of information distinct
- Business guidance: approved policies, procedures, and rules that shape how the agent should handle a task.
- Current evidence: information retrieved from authoritative records or observed through a live query or tool call.
- Memory: useful information retained from earlier interactions or work, subject to defined write and retention rules.
Microsoft Learn’s Agentic Behavior Engineering advises identifying the source and authority of business guidance, current evidence, and memory. Those categories should remain distinguishable in the agent’s inputs and downstream records. In particular, memory is not an authoritative business record and must not be allowed to supersede one.
Start by defining the agent’s job and its authority
Before selecting a retrieval method or connecting a tool, specify one concrete task and the outcomes that count as correct. Then establish which information and actions are within scope. This gives the design a way to distinguish a missing source, a stale answer, an unauthorized operation, and a reasoning or instruction failure.
Write down the operating boundaries
- Task and expected outcomes: define what the agent is responsible for and what a satisfactory result looks like.
- Authoritative sources: identify which systems own the relevant business facts, who owns their data, and how current information is recognized.
- Access: identify the users, agents, and services permitted to access each source; preserve user-level permissions when acting on a user’s behalf.
- Actions: state which operations are read-only, which can change data, and when approval or escalation is required.
- Ownership and intervention: assign responsibility for the agent and define how people can review, stop, or take over its work.
Microsoft Learn’s Data architecture for AI agents across your organization and Govern and secure AI agents across the organization address governed data, permission preservation, ownership, identity, security, residency, retention, and monitoring. AWS Prescriptive Guidance’s Agentic AI architecture in the enterprise describes application, agent, and service components, including knowledge bases, role-based access control, observability, and security. These are vendor-authored guidance documents, not a vendor-neutral product comparison.
Choose retrieval, tools, and memory for different needs
Retrieval, live tools, and memory solve different problems. Retrieval supplies relevant knowledge; a tool can query a current system or perform an operation; memory carries selected information across interactions. Choosing among them by task need makes authority, permission, and risk easier to control.
Rank #2
| Approach | Use it for | Controls to design |
|---|---|---|
| Built-in governed retrieval | Finding relevant information in approved enterprise sources when the built-in capability meets accuracy and compliance needs. | Source authority, access permissions, freshness, provenance, and auditability. Microsoft Learn recommends built-in retrieval by default when it meets accuracy and compliance requirements. |
| Custom retrieval | Cases where built-in retrieval does not satisfy the task’s established accuracy, compliance, or operational requirements. | Document why the built-in option is insufficient; preserve source permissions and provenance through indexing and retrieval; assess the added operational burden. Microsoft Learn discusses built-in retrieval and agentic retrieval but does not establish a universal performance ranking. |
| Live tools or connected services | Querying changing data or carrying out an operation that must happen in a connected system. | Use authenticated calls, authorize every call, specify read-only versus write access, preserve user-level permissions where relevant, and audit operations. MCP is one connection pattern discussed by Microsoft Learn for live data and actions. |
| Memory | Retaining selected task or interaction information that remains useful beyond the immediate exchange. | Define who or what may write it, what may be retained and for how long, how it is deleted, and how access and changes are audited. Keep memory identifiable and subordinate to authoritative records. |
A table cannot determine the right pattern by itself. Compare candidate designs against data authority and freshness, least-privilege access, action scope, provenance and audit trail, support for long-running work, evaluation and drift detection, and operational burden including latency and cost. The cited guidance does not provide a vendor-neutral benchmark or a comprehensive measured comparison among products.
Assemble context without losing source, authority, or freshness
Context assembly should preserve enough information to tell where a claim came from and whether it is suitable for the task. A correct-looking answer can still be unsafe if it was derived from an outdated note, a source the user cannot access, or a low-authority reference treated as policy.
Apply controls throughout the information path
- Identify source and authority. Distinguish approved business guidance from current records and from memory or other non-authoritative hints.
- Preserve provenance. Keep the origin of retrieved and remembered information identifiable in the agent’s working context and relevant logs.
- Check freshness against the task. For changing facts, retrieve current evidence from the authoritative system instead of treating earlier conversation or memory as current.
- Enforce access boundaries end to end. Maintain permissions across retrieval, indexing, reasoning, memory, and output. Do not assume that access to a source by the agent automatically means every user may receive its contents.
- Make uncertainty actionable. If authoritative evidence is missing, stale, or inaccessible, the agent should seek the right source, ask for clarification, or escalate rather than promote memory to fact.
Microsoft Learn’s Agentic Behavior Engineering emphasizes qualifying evidence by source, authority, and freshness; its guidance also states that memory must not override authoritative records or be treated as prompts or instructions.
Rank #3
Manage context and state across long-running work
A long-running task may outlast the context available in one model interaction. Treating the full transcript as durable state can be inefficient, while compressing it too aggressively can erase decisions or dependencies. The design needs a deliberate method for retrieving relevant material, carrying forward essential state, and testing what is retained.
Use just-in-time retrieval for material not always needed
Keep references to relevant material and fetch it when the task requires it, rather than loading every available document into every interaction. This can keep the active context focused, but the retrieved material still needs its authority, permissions, and freshness checked.
Compact history around task state
When interaction history approaches context limits, compact it into task-relevant information: active goals, decisions, unresolved issues, and dependencies. Anthropic discusses compaction and structured note-taking for agent workflows. Test compaction against representative traces; a summary that omits a constraint or unresolved issue can change later behavior.
Give durable memory explicit lifecycle rules
Define memory write permissions, retention duration, deletion, and audit policies. Keep durable notes separate from authoritative business data, and make clear when the agent must verify a remembered detail against a current source. Retention and data residency should also follow the organization’s applicable rules.
Enforce actions outside the model’s instructions
Instructions can describe the intended boundary, but they are not the enforcement mechanism. Implement identity, authorization, allowed data, allowed actions, approvals, and audit controls in the application and connected services. For each tool, make the permitted operation explicit; a read-only query and a write-capable action should not share an undifferentiated permission boundary.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Verify behavior before release and during operation
Reliability depends on a feedback loop, not a one-time prompt review. Specify expected behavior, test representative situations, observe real execution, classify failures, and update the governed system components that caused them.
Build a behavior contract and scenario library
Describe what the agent should do under normal conditions and at important boundaries: when evidence conflicts, a source is unavailable, a requested action exceeds its scope, or a human decision is required. Use representative scenarios to assess whether the agent follows the contract. Include cases involving permissions, stale information, memory, and write-capable tools where those apply.
Evaluate changes before release
Replay scenarios when changing instructions, retrieval, tools, memory handling, models, or policies. A change in any of these assets can alter behavior even if the application code is unchanged. Use results to determine whether the change meets the task’s defined outcomes and boundaries before releasing it.
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Use production evidence to improve the right asset
Monitor usage, errors, retrieval quality, policy decisions, latency, and cost. Review telemetry and feedback to classify failures, then update the appropriate scenario, knowledge source, behavior contract, or engineering control. Keep logs and memory only as long as necessary under organizational rules, and account for data residency.
Common failure patterns and their corrections
- Putting everything in the prompt: move changing facts to governed retrieval or live queries, and load material when it is relevant.
- Treating memory as truth: identify it as memory and verify important or changing facts against authoritative records.
- Building custom retrieval by default: first assess whether governed built-in retrieval meets accuracy and compliance requirements; record the reason for an exception when it does not.
- Letting the model instruction carry the security boundary: enforce identity, authorization, and action scope in the application and connected services.
- Compressing long tasks without testing: test summaries on representative traces and check that decisions, active goals, unresolved issues, and dependencies survive.
- Evaluating only the initial prompt: evaluate the behavior of the assembled system, including its sources, permissions, tools, memory, and changes over time.
What to validate in the target environment
Architecture guidance does not establish how every platform, configuration, or deployment will behave. Before deployment, validate the actual environment’s platform capabilities and any preview status, data residency, retention, and identity support. Also test the end-to-end permission path, audit records, retrieval freshness, and recovery or escalation behavior for the agent’s specific task.
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