Automation Anywhere Agentic Automation

Construct a tenant- and release-specific capability map that positions Automation Workspace, RPA, AI Agent Studio, Automation Co-Pilot, Document Automation, Process Discovery, CoE Manager, and Cloud Service using current product documentation.

Select between RPA, APIs, packaged integrations, and Cloud Service for a given enterprise task.

Evaluate a process candidate using discovery evidence, automation suitability, risk, and expected business value.

Create a sequence diagram showing how the Process Reasoning Engine, Mozart Orchestrator, and Context Intelligence Graph support an exception-driven agentic process.

Assess where Automator AI and Automation Anywhere Code can accelerate the development lifecycle without weakening engineering controls.

Compare Automation 360 public-cloud and private-cloud/on-premises topologies across scalability, latency, data residency, operations, and upgrade management.

Create an architecture decision record for a supplied deployment scenario covering platform services, runners, identity, networking, credentials, resilience, and release management.

Define a document type, extraction schema, sample set, and field-level acceptance criteria for a Document Automation use case.

Configure document classification for a mixed input set and verify that each document is routed to the correct extraction workflow.

Configure field and repeating-table extraction for a semi-structured business document in Document Automation.

Create an ingestion workflow that submits documents with traceable business metadata.

Configure human validation for low-confidence or policy-sensitive fields.

Implement deterministic normalization and business-rule validation for extracted values.

Route unreadable documents, missing fields, validation failures, and service failures through distinct exception paths.

Integrate validated document data and processing status into a downstream system of record.

Build a representative labeled benchmark set for a document extraction release.

Calculate field accuracy, precision, recall, straight-through-processing rate, and human-review rate from evaluation results.

Classify extraction failures as input-quality, OCR, classification, extraction, validation, or integration defects.

Tune field confidence thresholds using the relative business costs of false acceptance and false rejection.

Evaluate currently supported cloud and private-processing options against volume, latency, residency, availability, and operational requirements.

Define controls for document encryption, sensitive-data handling, retention, access, and auditability.

Create a production operating specification covering service-level objectives, telemetry, drift indicators, release gates, and rollback criteria.

Compare candidate large language models for a specified AI Skill using quality, latency, context-window, privacy, deployment, and cost requirements.

Configure a governed connection from AI Agent Studio to an approved large language model provider.

Write a prompt that produces output conforming to a defined JSON schema.

Build an AI Skill that applies the schema-constrained prompt to unstructured enterprise input.

Validate an AI Skill response for schema compliance, required evidence, and policy constraints before downstream automation consumes it.

Implement timeout, retry, fallback, and error-routing behavior for an LLM-backed AI Skill.

Run a compact evaluation set against an AI Skill and make a release decision using defined quality thresholds.

Define source, authorization, freshness, and provenance requirements for an enterprise RAG corpus.

Select chunking and metadata strategies for a collection of business documents.

Represent entities and relationships from a business process in a contextual intelligence graph.

Configure retrieval so that user identity and access rules constrain returned context, including tests for allowed and denied content.

Combine semantic retrieval and graph-derived context for a workflow question.

Design a grounded generation prompt that requires evidence-backed answers and citations.

Measure retrieval relevance, citation correctness, and answer groundedness with a representative test-question set.

Define the goal, instructions, tools, constraints, autonomy boundaries, and success criteria for an enterprise AI agent.

Build an AI agent in AI Agent Studio that invokes an approved automation tool.

Apply the Process Reasoning Engine to choose the next action in an exception-driven workflow.

Model agent and automation coordination through Mozart Orchestrator for a multi-step process.

Add human approval at a risk-based autonomy boundary.

Use process simulation to test an agentic workflow against normal, exception, and failure scenarios.

Expose a governed agentic workflow through Automation Co-Pilot and verify its access controls and execution trace.

Map Aisera Agent Composer, systems of agents, AI tools, reasoning and orchestration, LLM services, and platform services to their architectural responsibilities.

Design an Aisera RAG flow that uses governed enterprise sources and knowledge-graph context.

Define an LLM gateway policy for model selection, routing, fallback, privacy, latency, and cost.

Model a multi-step fulfillment flow using Aisera workflows, events, connectors, and agent orchestration.

Compare Automation Anywhere and Aisera capabilities to assign knowledge, conversation, reasoning, orchestration, and execution responsibilities without duplication.

Design an integration contract between Aisera and Automation Anywhere using APIs, events, identity propagation, idempotency, error handling, and auditable execution.

Define measurable business, quality, autonomy, and risk objectives for a document-centric agentic automation use case.

Produce a hybrid reference architecture that applies the chosen Automation Anywhere–Aisera responsibility split across Document Automation, contextual retrieval, agents, orchestration, deployment boundaries, and systems of record.

Create a threat model covering prompt injection, data leakage, excessive agency, poisoned knowledge, credential misuse, and unsafe tool execution.

Define end-to-end observability for document, retrieval, model, agent, automation, and business outcomes.

Execute a scoped red-team test against the reference solution's knowledge, model, identity, and tool boundaries.

Conduct a production-readiness review using architecture, security, reliability, evaluation, and support evidence.

Create a phased rollout and operating model covering ownership, governance, incident response, cost management, and continuous improvement.