An enterprise agent succeeds or fails on the information and capabilities available at each decision. Context engineering is the design of instructions, retrieved evidence, conversation state, memory, tool descriptions, permissions and intermediate results that enter that decision. More context is not automatically better. The objective is the smallest authorised context that is sufficient, current, structured and testable.

Executive perspective

The decision behind context engineering for enterprise AI agents is an operating-model choice, not a demonstration. Leaders need to define the business result, evidence, control boundary, cost and accountable owner before selecting detailed technology. Production success requires a transition path that remains safe when data is incomplete, dependencies fail or user behaviour differs from assumptions.

State the baseline, risk appetite and acceptance thresholds. Separate authoritative facts from assumptions and practitioner signals. Use bounded experiments to answer consequential uncertainties, and stop or redesign when evidence does not support the expected quality, security or economics.

Define the context contract

For each agent decision, specify required facts, allowed sources, freshness, user identity, task state, tools, output schema and evidence. Define what must never enter context. This converts an informal prompt into an operational interface.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Separate instructions from untrusted content

System and policy instructions must remain distinct from webpages, emails, files and retrieved text. Label source provenance and prevent external content from granting permission or changing policy. Models may read instructions inside documents; architecture must not treat them as trusted authority.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Design retrieval around the task

Retrieve business objects, passages or records that answer the decision, not simply the nearest vectors. Combine metadata, lexical, semantic and relationship retrieval. Apply permissions and authority before ranking. Include citations and freshness so the agent can reason about evidence.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Use working memory deliberately

Conversation history and intermediate results help continuity but can accumulate errors and irrelevant detail. Summarise state into structured fields, preserve critical decisions verbatim and set context budgets. Rebuild from authoritative state for long-running workflows rather than trusting an ever-growing transcript.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Define long-term memory boundaries

Separate user preferences, organisational knowledge, episodic task history and learned procedures. Require purpose, consent, retention, correction and deletion. Memory should not silently convert one user’s sensitive interaction into another user’s context.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Model workflow state outside the model

Store goals, steps, approvals, tool results, retries and status in a durable state machine or workflow system. The model can propose transitions, but code and policy should validate them. External state supports recovery, audit and deterministic limits.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Write tool descriptions as contracts

Describe purpose, inputs, outputs, side effects, permissions, cost, latency and failure. Narrow overlapping tools and validate arguments. Do not expose powerful generic execution when a task-specific function will work. Tool output is also untrusted input.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Control context size and ordering

Prioritise current policy, task facts and authoritative evidence. Remove duplicates and stale content. Use summaries, references and selective expansion. Test lost-in-the-middle behaviour and ensure critical constraints remain visible near the decision.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Handle multi-agent context transfer

Pass structured task state, evidence and authority rather than entire hidden transcripts. Define which agent owns the outcome and which can act. Prevent a low-trust specialist from escalating permissions through handoff. Preserve provenance across the chain.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Evaluate context, not only final answers

Test source selection, freshness, permission, instruction adherence, memory correctness, tool choice and state transitions. Create ablations that remove or corrupt context to reveal dependency. Track whether the model used the supplied evidence.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Secure and minimise sensitive context

Redact secrets, tokenise sensitive identifiers, restrict logging and separate tenants. Use task-scoped credentials and destination controls. Apply retention to prompts, caches, traces and memory stores. Context is a data asset and an exfiltration surface.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Monitor quality, latency and cost

Measure context tokens, retrieval success, tool calls, memory reads, cache use, latency, cost and task completion. Identify unnecessary context that increases expense without quality. Alert on abnormal context growth, repeated retrieval and cross-boundary access.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Select a context-engineering partner

Look for AI evaluation, data retrieval, workflow, security and product skills. Ask the partner to map one agent decision into a context contract and demonstrate failure tests. Prompt writing alone is not context engineering.

Convert this principle into a concrete specification, control, test, runbook or service-level objective. Give it one accountable owner and reviewers from the functions exposed to the outcome. Document the exception path, escalation and retained evidence so teams can operate the design under pressure.

Compare alternatives on outcome, delivery effort, recurring cost, latency, reliability, security, reversibility and cost of delay. A representative pilot should include failure and abuse cases and finish with an explicit go, change or stop decision. A showcase without thresholds does not reduce production risk.

Implementation scorecard

Track user or business outcome, adoption, accuracy, reliability, security exceptions, change lead time, unit cost and realised benefit. Give every metric a baseline, target, source, review cadence and owner. Segment results by tenant, workflow, risk tier or market so aggregate performance cannot hide concentrated failures.

Recommended engagement approach

Start with one representative workflow and produce the architecture, data and permission map, risk register, economic model, evaluation plan and staged roadmap. Project Supply can connect digital engineering, AI/data, ecommerce and cybersecurity delivery to move context engineering for enterprise AI agents from assessment into controlled production.