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Build a Jac AI agent as a small, controlled graph program rather than as an unconstrained prompt. Model users, tasks, conversations, and events as nodes and edges; use a stateful walker to validate a request and load only the graph paths it needs; expose narrow deterministic abilities as tools; and call the language model through a typed function declared with by llm(). Keep authorization, state changes, retries, and error handling in ordinary Jac code. This design gives the model useful context without giving it direct authority to change your data.
The first useful agent should handle one bounded job—such as looking up a task and, with permission, changing its status. Once that path is reliable, add durable event memory, limits, logging, and additional workflow stages.
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What are Jac and Jaseci, and why are they useful for an AI agent?
Jac is the programming language; Jaseci is the surrounding runtime and project ecosystem. Jac combines ordinary imperative programming with graph traversal and language-model-backed functions. The graph is not just a storage layer: it represents domain objects and conversation state that an agent can traverse deliberately.
In a conventional chatbot, context is often a prompt assembled from scattered records. In Jac, a request can enter at a known graph location, travel through explicit relationships, invoke deterministic abilities, and leave an auditable event behind. The walker that performs this work carries its own state while moving through the persistent graph, which makes it a natural unit for an agent or a multi-stage processing pipeline.
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The language-model boundary is intentionally narrow. A function declared by llm() can classify a request, extract fields, or propose an action in a declared shape. Jac code around that function decides whether the proposed action is allowed, executes the real operation, handles failures, and persists the result. The model suggests; the program remains in charge.
How should a Jac agent be decomposed?
Start by assigning one responsibility to each layer. This prevents a prompt from becoming an accidental permission system.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minute| Layer | Jac representation | Responsibility | What the model must not do |
|---|---|---|---|
| Domain and memory | Nodes and edges | Store users, tasks, messages, permissions, and events; express the relationships between them. | Invent or directly rewrite graph state. |
| Agent state | Walker fields and traversal | Carry the current request, identity, intermediate results, and stage while visiting the required graph paths. | Traverse the whole graph merely to find context. |
| Tools | Jac functions or abilities | Perform validated lookups, calculations, and updates with explicit input and output contracts. | Choose an arbitrary function, construct an unchecked query, or bypass authorization. |
| Model boundary | Typed function with by llm() |
Classify, plan, or extract a small structured result. | Commit side effects, issue retries, or decide permissions. |
| Workflow | One walker or several stage-specific walkers | Sequence intake, planning, tool execution, and finalization. | Hide a long chain of irreversible actions in one opaque prompt. |
There are two useful defaults. Keep routing deterministic when the request format is known, and use the model only where language understanding is needed. Keep memory persistent for facts and task events, but keep temporary scratch data on the walker so it does not pollute the graph.
What do I need before installing Jac?
Use the official Jac installation flow for your operating system and choose a supported Jac release. The command names below are the usual verification pattern; if your release uses a different launcher, use the command shown by its installer rather than forcing an older command.
- A supported Python runtime if the installer uses the Python package distribution.
- A project directory under version control.
- Credentials for the model provider you intend to call, stored outside source files.
- A local graph database or the persistence configuration supplied by your Jaseci deployment.
- A test model or mock response for development, so tests do not depend on a live model call.
python -m pip install jaclang
jac --versionRecord the version printed by the second command. Jac syntax and runtime commands can evolve, so keep that version beside your project and run the same version in CI and deployment. Create a small entry file, for example agent.jac, and run it with the launcher documented for that release, commonly:
jac run agent.jacDo not put an API key in a node, walker, prompt, or committed configuration file. Inject it through the runtime environment or a secret manager and make the model client fail closed when the credential is absent.
How do I model agent memory with Jac nodes and edges?
Represent durable facts and events as graph data, then give the agent a stable persistence anchor. A compact task assistant might use this model:
node User {
has id: str;
has display_name: str;
}
node Task {
has id: str;
has summary: str;
has status: str;
}
node TaskEvent {
has kind: str;
has detail: str;
has occurred_at: str;
}
edge owns {}
edge records {}
The exact declaration details can vary with the Jac release, but the design is stable: a user owns or can access tasks, and a task records events. Keep an event append-only when possible. An event such as status_changed should contain the old and new values, actor identity, request or correlation ID, and time; it should not silently overwrite the explanation of what happened.
Why is root important?
root is the persistence anchor from which a walker can reach application data. On first use, create or locate the user beneath that anchor; on later requests, traverse from the user to the task and its recent events. Do not make the model search the entire graph. Narrow traversal both reduces prompt size and limits accidental exposure of another user’s data.
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// Illustrative graph setup
spawn root ++> User(id=user_id, display_name=name);
user ++> Task(id=task_id, summary=summary, status='open');
task ++> TaskEvent(kind='created', detail='initial task', occurred_at=now);Use stable identifiers rather than display names for joins. Normalize timestamps and status values before they enter the graph, and reject unknown status strings at the boundary. If a fact is useful only during one turn—such as a candidate classification—keep it on the walker instead of persisting it as memory.
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How do I give a Jac agent safe tools?
Define two or three narrow abilities before adding any model call. Each ability should have a small input schema, a predictable output, and an explicit statement of side effects. A lookup tool can be read-only; an update tool must validate the target, the new value, and the caller’s permission.
def lookup_task(task_id: str) -> TaskResult {
// Validate format, fetch one task, and return a redacted result.
}
def set_task_status(task_id: str, new_status: str, actor_id: str) -> ToolResult {
// Check the allow-list of statuses and actor permission.
// Write the task and append one TaskEvent atomically.
}
Use an allow-list to map model-selected operation names to these functions. Never evaluate a name supplied by the model as code, and never pass a model-generated query directly to a database. Unknown operations should return a structured refusal that the walker can log and present to the user.
What makes a tool contract reliable?
- Inputs: require types, length limits, identifier formats, and enumerated values.
- Outputs: return a stable shape with a success flag, data or error code, and a correlation ID.
- Permissions: check the authenticated actor inside the tool, not only in a prompt or earlier stage.
- Side effects: label reads, writes, external calls, and irreversible operations separately.
- Idempotency: accept a request ID for writes so a retry cannot create duplicate updates or events.
Keep tools boring. Deterministic code is easier to test, audit, retry, and replace than a general-purpose “do anything” ability.
How do Jac walkers work in an agent workflow?
A walker is a mobile computation that maintains state as it traverses a persistent graph. For an agent, that state can include the authenticated user, the original request, selected context, a proposed action, tool output, and the final response. Traversal should be explicit: visit only the user, task, and event paths needed for the current request.
walker TaskAgent {
has request: str;
has actor_id: str;
has response: str;
can start with entry {
// 1. Validate request and actor.
// 2. Visit the actor's permitted task path.
// 3. Build a small context value.
// 4. Call the planner and execute an allow-listed tool.
// 5. Persist an event and set response.
}
}The can ... with entry form shown here illustrates an entry ability; use the equivalent entry syntax in the Jac version you install. Keep validation before traversal, and make traversal failures distinct from “no matching task.” That distinction is important for both user feedback and incident diagnosis.
How do I connect a Jac agent to an LLM with by llm()?
Declare one typed function for one model responsibility. Classification, action planning, and field extraction are good candidates; authorization and mutation are not. The function’s return type should contain only the fields the next deterministic step needs.
// Illustrative typed model boundary
def choose_action(request: str, context: str) -> Action by llm();An Action value can contain an operation kind, an optional task identifier, an optional new status, and a user-facing explanation. Define the record or object using the data-declaration syntax supported by your Jac release. Keep the prompt assembled from trusted system instructions plus the narrowly selected graph context. Treat text retrieved from a task, email, or document as untrusted content; it may contain prompt-injection instructions.
What should the planner be allowed to return?
Use a finite set such as lookup_task, set_task_status, and answer_from_context. Require empty or null fields for operations that do not use them, and reject malformed combinations. If the model returns an unknown operation, an invalid identifier, or a status outside your enum, stop before any tool call and record a validation error.
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Set model temperature and token limits according to the task, but do not rely on those settings for safety. A deterministic validator and an allow-list are the safety boundary. If the model times out or returns invalid data, the walker should return a retryable error or ask the user to clarify rather than guessing.
How do I execute the selected tool and persist an event?
Keep planning and execution as separate operations. The walker receives the typed action, checks it against policy, calls exactly one mapped ability, and appends a concise event describing the outcome.
can execute(action: Action) -> ToolResult {
if action.kind == 'lookup_task' {
return lookup_task(action.task_id);
}
if action.kind == 'set_task_status' {
return set_task_status(action.task_id, action.status, actor_id);
}
return ToolResult(error_code='unknown_operation');
}Before calling a write tool, verify that the task belongs to the actor or that the actor has an explicit permission. Re-check the current task version if concurrent updates are possible. After success, persist an event containing the operation, outcome, actor, and correlation ID; after failure, persist a failure event without copying secrets or an entire prompt into durable memory.
Return a response assembled from trusted tool output. The model may help phrase that response, but it should not be able to claim that a write succeeded when the tool returned an error. A useful pattern is to render success and failure templates in Jac and insert only validated fields.
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How do I retrieve memory on a later request?
On a second request, locate the user from root, follow the permitted edge to the task, and load a bounded set of recent events. Summarize or compact old events with deterministic code when the history grows; do not dump an unbounded transcript into every model call.
- Authenticate the actor and resolve the stable user identifier.
- Traverse from
rootto that user and verify access. - Follow the relevant task edge and select only the fields needed for the request.
- Load recent events in a fixed order and cap their count or total characters.
- Pass the resulting context to the typed planner.
This gives the agent durable memory without treating every historical message as equally authoritative. Mark facts with their source and time where that matters. If a user asks to delete or correct personal data, implement that as a deterministic data-governance operation rather than asking the model to “forget.”
When should I split the agent into multiple workflow stages?
A single walker is adequate for a short, synchronous task. Split the workflow when it has distinct validation, planning, execution, approval, or finalization stages, or when a tool can take long enough to require a queue.
Intake stage
Authenticate the caller, normalize the request, enforce size limits, and load the minimum graph context. Reject malformed requests before invoking the model.
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Call one by llm() function to classify or propose an allow-listed action. Persist the plan only if you need approval, replay, or asynchronous execution; otherwise keep it on the walker.
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Execution stage
Run the mapped deterministic ability. For a queued job, create a command node with pending status and an idempotency key. A worker walker claims that command, performs the operation, and records success or failure.
Finalization stage
Append the event, update the command status, and render a response from verified data. If human approval is required, stop after planning and expose a pending state rather than allowing the model to approve itself.
Separate walkers also make retries safer: you can retry a failed model call without repeating a completed write, or retry a tool with an idempotency key without creating duplicate effects.
Which Jac agent design choices matter most?
Choose deliberately among these common alternatives; none is universally best.
| Decision | Safer default | Use the alternative when | Main risk |
|---|---|---|---|
| Routing | Deterministic rules for known commands, model routing for natural-language ambiguity. | Users express the same intent in many forms and a classifier has a measurable benefit. | Model-driven routing can select an unintended tool. |
| Memory | Persistent graph nodes for durable facts and events; walker fields for scratch state. | A value is temporary, sensitive, or not worth retaining. | Persisting every message increases exposure and retrieval noise. |
| Tool execution | Synchronous for short, idempotent reads and writes. | Work is slow, rate-limited, or needs approval; use a queued command. | Retries can duplicate side effects without idempotency. |
| Permissions | Narrow per-tool checks and an explicit operation allow-list. | Broader access is unavoidable, in which case add approval and stronger auditing. | Prompt-only permissions are not enforcement. |
| Agent count | One walker for one bounded job. | Separate specialists have different data access, latency, or failure policies. | Multiple agents add coordination, state, and debugging complexity. |
How do I secure a Jac agent against prompt injection and unsafe actions?
Assume that both the user request and retrieved documents can contain hostile instructions. Keep system policy outside retrieved text, label untrusted content, and never let a document redefine the operation allow-list. The tool layer must enforce the same policy even if the model ignores the prompt.
- Authenticate every request and carry the actor identity on the walker.
- Authorize at the point of data access and again immediately before a side effect.
- Redact secrets and unnecessary personal data before constructing model context.
- Limit prompt size, tool arguments, traversal depth, and execution time.
- Require confirmation or a human approval node for destructive or high-value actions.
- Log model version, prompt or context identifiers, selected operation, tool result, and policy decision without storing raw secrets.
Use separate credentials and graph namespaces for development, staging, and production. Rotate model-provider keys independently of application releases, and make a missing or expired key produce a controlled outage rather than a fallback to an unapproved provider.
How should I test and operate the agent?
Test the deterministic parts first. Unit-test node validation, traversal filters, permission checks, status transitions, idempotency, and event creation without a model. Then test the model boundary with fixtures that return valid, malformed, unknown, and adversarial actions.
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- A user asks for a task they do not own.
- The planner returns an unknown operation or an invalid status.
- The same write request is delivered twice.
- The graph contains an injected instruction in a task description.
- The model times out, exceeds its output limit, or returns invalid structured data.
- A tool succeeds but event persistence fails, or vice versa; verify the transaction or compensating behavior.
Give every request a correlation ID and include it in walker state, tool calls, graph events, and logs. Track latency separately for graph traversal, model calls, and tools. A replayable event record lets you diagnose a bad plan without rerunning a side effect. Before deployment, pin the Jac and model-client versions, configure persistence and secrets through the runtime, and exercise a recovery path for a failed worker or unavailable model.
What are the most common Jac agent mistakes?
Making the model the workflow engine
A giant prompt that asks the model to inspect data, choose tools, authorize itself, and explain the result is difficult to constrain. Move each enforceable decision into Jac and leave the model only the language-dependent step.
Creating a single “万能” tool
A tool that accepts arbitrary code, URLs, queries, or operation names defeats validation. Replace it with several small abilities whose arguments and effects can be reviewed independently.
Using the graph as an unbounded transcript
Persisting every token makes retrieval slow and can expose irrelevant or sensitive content. Store durable facts and concise events, then select a bounded context path for each request.
Allowing writes without idempotency
Network retries and worker restarts are normal. Require a request key for mutations and record the key with the event so duplicate deliveries become safe no-ops.
Skipping version checks
Jac and the surrounding runtime can change syntax and launcher behavior. Record the installed version, run the same version in automation, and adapt illustrative snippets to the release’s parser and deployment configuration.
What is the smallest useful Jac agent to build first?
Build a task assistant with one user node, one task node, an event edge, two tools, one intake walker, and one typed planner. The planner may choose only “look up task,” “change status,” or “answer from the selected task context.” The walker validates identity, traverses from root, calls the planner, checks the allow-list, executes one tool, and records one event.
Demonstrate two requests: the first creates or updates a task; the second retrieves the persisted task and event state. Only after that path passes authorization, malformed-output, retry, and audit tests should you add queues, approvals, additional graph domains, or another agent.
How do I know the design is ready for production?
A production-ready Jac agent has a bounded purpose, explicit graph relationships, narrow typed tools, a single controlled by llm() boundary for each model task, and deterministic enforcement around it. It can explain which graph data it used, which operation it selected, whether the operation was authorized, and what was actually written. If any of those answers depends solely on the model’s claim, the boundary is still too broad.
Frequently Asked Questions
Can a Jac agent work when the language model is unavailable?
Yes. Keep deterministic commands and previously stored facts usable without a model, and return a clear “model unavailable” state for requests that require classification or extraction. A cached plan should never be replayed for a new authorization context without rechecking permissions.
Where should I store conversation transcripts in a Jac graph?
Store a transcript only when retention is a stated product requirement. Prefer a separate conversation or message node set with retention, redaction, and access rules, while keeping task events as concise operational records.
How can I add human approval to a Jac workflow?
Persist a command or approval node containing the proposed action, actor, expiry, and policy reason. Pause the walker, let an authenticated reviewer approve or reject it, then have a deterministic worker revalidate the action before execution.
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Capture the structured validation error and a correlation ID, then retry only within a small fixed budget. If failures continue, route to a clarification response or deterministic fallback; do not broaden the tool schema just to accept malformed output.
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