Where does agent context live?

Agent context is the information a coding agent uses to understand its goal, constraints, relevant systems, and evidence of success. It includes written artifacts such as issues, docs, code, and test results, but it also includes decisions and operational knowledge that are often left unsaid. The hard problem is not giving an agent every available source. It is selecting enough trustworthy context for the task while keeping access bounded and auditable. An agent-first control plane such as Levr can make tasks, acceptance criteria, verification, and history available as shared working context. That foundation lets teams grant capability deliberately, ask for missing evidence, and retain human approval for consequential changes.

At a Glance

Context is a living operational resource, not a folder of documents.

• Where it lives: Useful project knowledge is spread across code, issues, documentation, test results, conversations, and individual experience.
• The challenge: Coding agents need relevant context, not unrestricted access to every system and source.
• The safety boundary: Background agents require constrained permissions, explicit tasks, verification, and accountable approvals.
• The key capability: A capable agent should be able to identify missing information and request it before taking action.
• The operating model: Teams need a shared record of intent, work, evidence, decisions, and ownership.

Why is agent context more than documentation?

Agent context extends beyond documentation because important engineering knowledge is distributed across systems and people. A task description may explain what to build, while code history, test outcomes, incident notes, and informal decisions explain why it should be built that way and what constraints matter.

A repository and knowledge base are useful starting points, but they rarely contain the whole operating picture. A developer might understand a fragile integration from a past outage. A product manager may have clarified a requirement in a short conversation. An engineering manager may know that a deadline, customer commitment, or security boundary changes the acceptable solution.

That information affects implementation decisions. If it never enters the work system in some usable form, a coding agent cannot reliably account for it.

This does not mean teams need to capture every conversation word for word. It means they should identify the decisions that materially change scope, architecture, quality requirements, ownership, or risk. Those decisions belong near the work they govern.

Context categories that commonly matter

• Task context: The desired outcome, acceptance criteria, priority, and definition of done.
• Technical context: Repository structure, APIs, dependencies, architecture, conventions, and implementation history.
• Quality context: Test coverage, known regressions, CI/CD signals, release constraints, and expected behavior.
• Business context: User impact, deadlines, policy requirements, customer commitments, and tradeoffs.
• Human context: Decisions, assumptions, concerns, and domain knowledge that have not yet been formalized.
  

What makes context difficult for coding agents?

Context is difficult because it is incomplete, inconsistent, and constantly changing. Coding agents can process large amounts of material, but more information does not automatically create better decisions. Irrelevant, stale, conflicting, or unsafe inputs can reduce reliability instead of improving it.

There are two common failure modes. The first is context starvation, where an agent receives a vague instruction but lacks the code, criteria, constraints, or examples required to execute safely. The second is context overload, where the agent gets broad, unfiltered access to content that is unrelated, outdated, contradictory, or sensitive.

Both failures produce predictable results. In the first case, the agent fills gaps with assumptions. In the second, it spends effort sorting weak signals from useful evidence or follows instructions that should not have been treated as authoritative.

The answer is not a universal context bundle. Context should be selected for the task at hand, linked to a clear source, and refreshed when the task or environment changes.

The Model Context Protocol architecture specification describes a model in which hosts, clients, and servers establish controlled connections to tools and resources. That architecture is useful because it frames context access as a deliberate system boundary, rather than an ungoverned collection of integrations.

How should teams decide what context an agent needs?

Teams should begin with the task, then provide the minimum context required to perform it safely and verify the outcome. The right bundle includes the goal, constraints, relevant sources, allowed actions, and evidence needed to decide whether the work is complete.

A practical approach is to treat each task as a context contract. Before assigning work, define what the agent needs to know, what it may access, what it may change, and how success will be evaluated.

A five-part context contract

1. Intent: State the outcome in concrete terms. For example, “Add a validation rule that blocks duplicate workspace names.”
2. Constraints: Specify boundaries such as compatibility requirements, security rules, performance expectations, or prohibited files.
3. Relevant evidence: Link the issue, affected services, APIs, design decisions, existing tests, and related incidents.
4. Allowed actions: Define whether the agent may read code, edit a branch, run tests, create a pull request, or modify external systems.
5. Verification: Identify the tests, review gates, and approval requirements that prove the task is done.

Consider a request to update an authentication flow. The relevant context is not every document in the company. It is the affected service, identity-provider configuration rules, existing authentication tests, known security constraints, the acceptance criteria, and the required approval path. That targeted set is both more useful and safer.

Can an agent identify missing context by itself?

Yes, an agent can help identify missing context when it is given a clear task and permission to ask for more information. Instead of silently guessing, it can compare the available evidence with the work required and request specific documents, access, examples, or decisions.

This is an important shift in context engineering. Humans often assume they must predict every source an agent will need before work begins. That can work for routine tasks, but it breaks down when the work crosses unfamiliar systems or depends on hidden assumptions.

A better pattern is staged discovery:

• Give the agent a bounded task and an initial set of trusted sources.
• Require it to explain what is missing before expanding its scope.
• Review requests for additional data or permissions against the task.
• Record approved sources and decisions alongside the work item.

For example, an agent asked to fix a failing payment test might determine that it needs the relevant service configuration and the API contract. It should request those artifacts specifically, rather than receiving unrestricted access to production credentials, internal financial documentation, or unrelated repositories.

Context discovery should not become a shortcut around access controls. An agent can identify a need, but a human or policy system should decide whether the requested context is appropriate to share.

Give agents context that remains connected to the work.

Levr keeps issues, acceptance criteria, tests, results, pull requests, and attributed activity in a shared control plane, so agents and humans can work from the same operational record.

Explore Levr's agentic workflow

What must be true before an agent can work in the background?

A background agent needs more than infrastructure and tool access. It needs constrained context, least-privilege permissions, observable actions, verification gates, and a clear escalation path. Without those controls, automation can move faster than the team’s ability to understand, correct, or approve its work.

Running an agent in the cloud can make continuous work possible. It can monitor tasks, prepare changes, investigate failures, and coordinate with connected systems. But persistent access raises the stakes because the agent may encounter changing inputs, new instructions, and sensitive environments without a person present at every step.

Safe background operation depends on structural controls, not just better prompt wording. The NIST AI Risk Management Framework emphasizes managing AI risks across design, deployment, and use. For engineering teams, that translates into clear boundaries around what an agent can access, what it can execute, and how its output is reviewed.

Minimum controls for background coding agents

• Scoped credentials: Grant only the repositories, environments, and services required for the assigned task.
• Action limits: Separate read, write, deploy, and administrative capabilities instead of treating access as all or nothing.
• Sandboxed execution: Run untrusted code, external inputs, and experimental changes in isolated environments when possible.
• Verification gates: Require tests, policy checks, and reviews before work reaches a completed or deployed state.
• Activity records: Capture what the agent accessed, changed, tested, and reported.
• Escalation rules: Define when the agent must stop and ask for input, such as ambiguous requirements, failed tests, or requests for broader access.
  

How do shared project systems improve agent context?

Shared project systems improve agent context by linking intent, implementation, testing, and approval in one operational record. Instead of relying on disconnected prompts and status updates, agents can read the current task state, act on defined criteria, record evidence, and hand off work with traceability.

Traditional workflows often split the critical record across issue trackers, chat threads, source control, test systems, and personal knowledge. That fragmentation creates drift. The issue says one thing, the pull request reflects another, and the actual decision may exist only in a message someone remembers.

One way to reduce that drift is to make the task the shared object that both humans and agents update. In Levr, work can be organized around issues and structured acceptance criteria, while tests and results remain linked to the same underlying work. This helps connect planned behavior to the evidence that validates it.

A useful operating sequence is:

1. Define intent and acceptance criteria in natural language.
2. Assign or route the task to a coding agent with the required context.
3. Run automated verification and attach results to the work.
4. Review the change, evidence, and remaining risks before approval.

This model supports different levels of autonomy. A team can keep agents in a strictly advisory role, allow them to prepare branches and tests, or let them execute defined workflows with approval gates. The shared control plane stays useful in each case because it preserves context and accountability.

What mistakes create weak or unsafe agent context?

Weak agent context usually comes from treating access as a substitute for understanding. Giving an agent broad permissions, a large document dump, or a vague prompt may feel efficient, but it hides uncertainty and makes it harder to verify why the agent made a particular decision.

1. Assuming written docs contain everything

Documentation is essential, but it is rarely complete. Important decisions often remain in conversations, prior incidents, and individual experience. Capture the decisions that affect the current work instead of assuming the agent can infer them from repository files alone.

2. Giving unrestricted access “just in case”

Broad access expands the blast radius of mistakes and exposes irrelevant or sensitive information. Start with the least context and permission needed, then expand only when the agent can explain why the additional source is relevant.

3. Treating all sources as equally trustworthy

An issue, a runbook, a generated summary, a public webpage, and a comment in a code file do not carry the same authority. Teams should identify authoritative sources and design workflows that prevent untrusted content from silently becoming executable instructions.

4. Leaving acceptance criteria ambiguous

An agent cannot reliably verify “make it better” or “fix the onboarding flow.” Define observable outcomes, expected behavior, tests, edge cases, and the human decision-maker for tradeoffs that cannot be automated.

5. Separating tests from implementation context

If test results live somewhere unrelated to the issue and pull request, it becomes harder to establish whether the task is actually complete. Link validation evidence directly to the acceptance criteria and change being reviewed.

How can engineering managers make tacit knowledge usable?

Engineering managers can make tacit knowledge usable by converting high-impact assumptions and decisions into lightweight, task-linked records. The goal is not to document every conversation. It is to preserve the context that changes implementation choices, risk, priorities, or release readiness.

Start with recurring knowledge gaps. If engineers repeatedly ask why a service has an unusual constraint, why a migration must happen in a specific order, or why a test is intentionally strict, that knowledge should become discoverable.

Useful practices include:

• Decision notes: Record the decision, alternatives considered, owner, date, and downstream implications.
• Incident links: Connect past failures to the services, tests, or constraints they influenced.
• Structured handoffs: Capture current state, unresolved questions, risks, and next actions when work changes hands.
• Context interviews: Ask domain experts to explain assumptions before a major agentic workflow depends on them.
• Review feedback capture: Turn repeated pull request comments into explicit conventions, criteria, or automated checks.

The result is not perfect institutional memory. It is a more reliable path from human knowledge to project context that can be used by both people and coding agents.

Technical Deep Dive FAQ

What is agent context?

Agent context is the set of information, instructions, resources, constraints, and current state that a coding agent uses to complete a task. It can include an issue description, repository files, architecture documentation, test results, tool outputs, and approval requirements. Good agent context is relevant, current, traceable, and limited to what the task requires. It is not simply a large prompt or unrestricted access to company systems. Context determines what an agent can understand, what it is allowed to do, and how its work can be evaluated.

What is context engineering?

Context engineering is the practice of designing how relevant information reaches an AI or coding agent during work. It includes selecting trusted sources, structuring tasks, setting permission boundaries, retrieving task-specific materials, maintaining current state, and defining verification criteria. The goal is to provide enough information for reliable action without creating noise, exposing unnecessary data, or allowing ambiguous sources to influence important decisions. In agentic development, context engineering is as much about workflow design and governance as it is about prompts.

How much context should a coding agent receive?

A coding agent should receive the minimum context needed to understand the requested outcome, make a safe change, and verify it. That commonly includes the task, acceptance criteria, affected code, relevant interfaces, existing tests, constraints, and approved tools. More context is not always better. Large, unrelated inputs can introduce stale assumptions, distract from the task, and increase security exposure. Start with a scoped context bundle, then allow the agent to request specific additional sources when it can explain their relevance.

Can coding agents use information from chat or meetings?

Yes, but informal knowledge should be converted into reviewable project artifacts before it becomes operational context. A short decision note, clarified acceptance criterion, linked incident summary, or recorded constraint is more reliable than treating a chat thread as permanent truth. Teams should avoid automatically ingesting all conversational content into agent workflows. Instead, capture the specific decision, its owner, the date, and its impact on the task. This keeps the context useful while limiting noise, privacy concerns, and ambiguous instructions.

What is the difference between context and permissions?

Context determines what an agent knows, while permissions determine what it can access or change. They are related but should be controlled separately. An agent might know that a production configuration exists without having permission to read or modify it. Likewise, an agent may have access to a repository but lack the contextual information needed to make a safe change there. Strong agentic workflows define both: the information required for the task and the least-privilege actions allowed to complete it.

How do you prevent agents from using stale context?

Prevent stale context by linking agents to current sources of truth, recording timestamps and ownership, and requiring verification against the current codebase and test state. Avoid copying large documents into prompts where they become detached from updates. Instead, reference maintained issues, versioned documentation, pull requests, CI results, and decision records. For high-impact tasks, agents should summarize the assumptions they are relying on and flag conflicts between sources. Human reviewers can then confirm whether the context remains valid before approving consequential changes.

Should background agents have production access?

Background agents should not receive broad production access by default. If a workflow genuinely requires production interaction, permissions should be narrowly scoped to the smallest action needed, constrained by environment, logged, and protected by approval or policy gates. Read-only observability access is different from write access, and both should be evaluated separately. Teams should also define stop conditions for unexpected states, failed checks, or requests for expanded permissions. Background execution is safest when reversible actions and verified evidence come before irreversible changes.

How can teams evaluate whether an agent had enough context?

Teams can evaluate context quality by reviewing whether the agent understood the task, used authoritative sources, surfaced assumptions, requested missing information, and produced work that passed defined verification. Repeated failure patterns are useful signals. If agents frequently misunderstand ownership, violate conventions, miss edge cases, or require extensive correction, the issue may be missing or ambiguous context rather than model capability alone. Track which inputs were available for successful and unsuccessful tasks, then improve task templates, decision records, tests, and access boundaries over time.

How does MCP affect agent context management?

MCP, short for Model Context Protocol, provides a standardized approach for connecting AI applications to external tools and resources. It can make context available through well-defined servers instead of ad hoc integrations. However, MCP does not automatically make context safe or correct. Teams still need to assess source trust, permission scope, input handling, logging, and operational ownership for each connected capability. A protocol can improve interoperability, but engineering controls determine whether the resulting workflow is reliable and appropriately constrained.

Why should tests be part of the agent context?

Tests provide concrete evidence of expected behavior, regressions, and completion criteria. Including relevant tests in agent context helps a coding agent understand how the system is intended to behave instead of relying only on prose requirements. Tests should not be treated as the only source of truth, since they can be incomplete or outdated, but they are a critical validation layer. Linking tests to acceptance criteria also gives reviewers a clearer basis for determining whether a change satisfies the task before it is approved or released.

At a Glance

Agent context works when it is selective, current, and connected to verification.

• Context is distributed: Important knowledge lives in artifacts, systems, conversations, and people.
• Scope matters: Give agents relevant information and least-privilege access rather than unlimited visibility.
• Discovery helps: Agents can identify and request missing context, but policy and human review should control expansion.
• Background work needs controls: Constrained actions, logs, tests, gates, and escalation paths make autonomy manageable.
• Shared records reduce drift: Keep intent, implementation, tests, and approvals connected to the same work.

The goal is not to make every piece of human knowledge machine-readable. It is to make the decisions that matter available, trustworthy, and accountable.

Further reading

• Model Context Protocol architecture specification
• NIST AI Risk Management Framework
• Anthropic prompt engineering documentation
• Levr agent-first project control plane
• Levr features for agentic development teams
• How Levr's agentic workflow operates
  

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Levr connects coding agents including Claude Code, Cursor, Codex, and Copilot to shared project context with issues, gates, test suites, and attributed activity. Free access is available during beta.

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