Automatan engineers the knowledge, memory, instructions, constraints, state, and tool environments that transform general-purpose AI models into reliable, domain-specific enterprise agents.
Dynamic context assembly · Persistent agent memory · Governed execution
Foundation models understand the world. They do not automatically understand your organization, customers, terminology, policies, workflows, decision criteria, or operational boundaries.
A foundation model can reason broadly. It cannot infer which internal policy applies, which source is authoritative, or how your organization makes a specific decision.
Prompts cannot reliably maintain organizational memory, resolve changing knowledge, preserve workflow state, or govern tools across persistent agent workflows.
Context Engineering continuously assembles the knowledge, memory, rules, state, and permissions required for domain-specific reasoning and execution.
Each layer gives the agent a different form of organizational understanding—assembled dynamically around the task it needs to perform.
Connect documents, databases, applications, knowledge bases, conversations, and approved external sources through semantic retrieval and structured knowledge relationships.
Maintain short-term reasoning context, interaction history, workflow memory, user preferences, organizational knowledge, and long-running agent state.
Provide role definitions, domain terminology, policies, procedures, approval structures, customer context, decision criteria, and function-specific operating rules.
Set the objective, analytical framework, constraints, evidence requirements, risk thresholds, exceptions, and decision boundaries governing the agent’s reasoning.
Expose the right APIs, applications, workflows, and actions for the task—along with permission boundaries, preconditions, approval requirements, and execution limits.
Six capabilities compound into a reusable foundation for enterprise AI agents.
Construct the right context at runtime based on the user, objective, workflow state, domain, risk, and available evidence.
Use Agentic RAG to reformulate queries, pursue relevant sources, validate retrieved information, and close context gaps before reasoning begins.
Maintain user, workflow, organizational, and long-term agent memory without forcing every interaction to begin from zero.
Connect entities, concepts, documents, policies, customers, products, and data relationships through domain-aware knowledge structures.
Preserve where the agent is in a process, what has already happened, which dependencies remain, and what event should occur next.
Make agents aware of available tools, access rights, action limits, approval requirements, and the consequences of execution.
Automatan evaluates the context supplied to every agent—so the system can determine what is relevant, authoritative, current, incomplete, or conflicting before the model reasons.
Define which systems, documents, policies, and records can ground the agent for each domain and decision.
Retrieve the information required for the current objective without overwhelming the context window with unrelated knowledge.
Ground agents in the correct policy, document version, customer state, workflow stage, and time-sensitive operational information.
Identify contradictory sources, missing prerequisites, incomplete records, and unresolved context before the agent produces a conclusion.
Record which sources, memories, instructions, rules, and tools were available to the agent for each output or action.
The same request can require a different response depending on the customer, policy, history, risk, and action boundaries surrounding it.
The agent retrieves the customer’s account tier, purchase history, contract terms, previous tickets, unresolved product issue, current refund policy, service-level obligations, and prior negative experience with automated support.
Refunds above the approved threshold require human authorization. High-value accounts with repeat unresolved issues must be routed to the retention team before a transactional response is issued.
Order retrieval, entitlement verification, refund calculation, response drafting, case routing, and manager-approval workflows.
The agent does not issue a generic refund response. It prepares the account context, calculates the applicable entitlement, and routes the case to the appropriate human with the relevant evidence attached.
TLS 1.3, AES-256 encryption, role-based access controls, and enterprise SSO/SAML.
Governed context pipelines designed for repeatable agent behavior across persistent, high-value workflows.
Zero-retention options and no training on customer data. Memory and context remain governed by enterprise policies.
Deploy through SaaS, private VPC, on-premise environments, APIs, and enterprise SDKs.