Context Engineering for AI Agents: The Layer Between Your Data and the Workflow

Prompt engineering asks, “What should we say to the model?”

Context engineering asks, “What should the model be allowed to know, remember, infer, and do for this workflow?”

That distinction matters once AI agents move beyond drafting text. A tool-using agent needs the right evidence, current state, policy constraints, role permissions, memory boundaries, and approval rules before it acts. Better wording cannot compensate for bad context.

Context packages, not context piles

Many teams start by connecting every source they can reach: CRM, docs, Slack, email, tickets, product usage, contracts, billing, and spreadsheets. That creates a large context pile. It does not create agent readiness.

A context package is smaller and more intentional. It is assembled for a specific workflow step and includes the evidence, constraints, and next-action options the agent needs.

For a support escalation, the package might include current ticket state, account tier, service-level commitment, open incidents, prior escalations, applicable policy, sentiment signals, and allowed escalation actions.

For a renewal-risk workflow, it might include contract terms, usage trend, support burden, executive sponsor notes, payment status, risk policy, and a recommended next step for RevOps review.

The package should make missing or conflicting context visible. A good package says, “Here is the evidence, here is what is stale, here is what conflicts, and here is what the workflow is allowed to do next.”

The mechanics of context engineering

flowchart LR
    A["Workflow step"]
    B["Required evidence"]
    C["Retrieval and source policy"]
    D["Permission filter"]
    E["Memory scope"]
    F["Tool constraints"]
    G["Agent action"]
    H["Feedback"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    H --> C

The loop at the end is not decorative. Rejected recommendations, missing facts, stale sources, and approval edits should improve future context packages.

NIST’s AI Risk Management Framework makes context engineering a risk-management discipline, not a prompt-writing exercise. “Map” means naming the evidence an agent can use. “Measure” means checking whether the context package was complete, current, cited, and permissioned. “Manage” means changing retrieval, memory, tools, or approvals when the workflow produces a bad recommendation.

OWASP’s 2025 LLM Top 10 matters because several of its risks live in the context layer: prompt injection inside retrieved content, sensitive information disclosure, supply-chain exposure, data or model poisoning, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. Context engineering is how the business decides which sources are trusted, which retrieved text is treated as evidence rather than instruction, which tools can be called, and which actions require a human gate.

McKinsey’s 2025 State of AI reinforces the operating point: scaled value comes from redesigned workflows with leadership ownership and human validation, not from making a model available in isolation. Metacto’s Context Engineering makes that redesign tangible through Context, Intelligence, and Control layers.

Retrieval policy is a business rule

Retrieval policy is often treated as a technical tuning problem. Some of it is technical. Much of it is operational.

The workflow has to decide:

• Which sources are eligible for this step?
• Which sources are authoritative?
• Which sources are useful but not authoritative?
• Which sources require citation?
• Which source conflicts force escalation?
• Which content should be excluded because of user role, tenant, geography, or sensitivity?
• Which retrieved content is untrusted instruction-like text that the agent must not obey?

These rules should be written down before teams tune chunk sizes or ranking parameters. Otherwise, the retrieval system may become very good at finding context the workflow should not use.

Memory needs expiration

Agents often need memory. They may need to remember that a reviewer approved an exception, that a customer requested a specific accommodation, that a prior workflow step already verified a source, or that the same ticket was escalated yesterday.

But memory is dangerous when it has no scope.

Memory should have:

• Owner: who approved or created it?
• Scope: which account, case, user, workflow, or tenant does it apply to?
• Source: where did it come from?
• Expiration: when should it be rechecked?
• Sensitivity: who can retrieve it?
• Conflict behavior: what happens if current source data disagrees?

Without those fields, memory becomes hidden state. Hidden state is where agents become hard to debug.

Tool constraints belong in the context design

Context engineering also defines what the agent is allowed to do after it reasons.

An agent may be allowed to draft a note, but not send it. It may be allowed to update a ticket status, but not issue a refund. It may be allowed to propose a CRM field update, but only after a manager approves the diff. It may be allowed to retrieve contract language, but not expose restricted pricing terms to every user.

Those constraints should be part of the context package so the model is reasoning inside the same boundary the system will enforce.

Context engineering changes evaluation

If a model gives a bad answer, the cause may not be model quality. It may be missing evidence, stale retrieval, weak source priority, bad permissions, vague tool constraints, or memory that should have expired.

That means evaluations should separate:

• Did the context package include the required facts?
• Were the facts current and cited?
• Did the package exclude restricted data?
• Did the model use the evidence correctly?
• Did the proposed action respect the tool boundary?
• Did the reviewer accept, edit, reject, or escalate?

This is how context engineering connects to Continuous AI Operations. The workflow should improve not only by changing prompts or models, but by improving the context that reaches them.

The build sequence

Start with one workflow step. Define the experienced operator’s evidence standard. Map sources and authority. Add permissions. Decide memory scope. Define allowed actions. Build a context package. Run it in review mode. Log edits and rejections. Improve the package before adding autonomy.

This is slower than connecting everything to a model. It is faster than recovering from an agent that acts on the wrong context.

Metacto’s Context Engineering practice exists for this middle layer: not raw data, not generic prompts, but prepared workflow context. It is the layer that lets AI Agents & Workflows operate with evidence and control.