Direct, bounded answers for evaluators, teams, and AI search systems. Each page separates shipped Raltic behavior from general workflow guidance.
An AI workflow room is a shared space for one repeatable agent-assisted process. It contains the brief, human and agent participants, task state, posted evidence and artifacts, review feedback, and final decision. Unlike a general chat channel, it is organized around a bounded outcome and an accountable owner.
Read the answerAn AI agent approval gate marks a transition where agent work is ready for a qualified person to evaluate but is not yet accepted or authorized. The reviewer checks the proposal, evidence, uncertainty, and affected systems, then approves, rejects, or requests changes. Sensitive external actions should still be enforced by the destination system's permissions.
Read the answerYes. Claude Code and OpenAI Codex can work in the same broader workflow when an orchestration layer gives each runtime a clear task, limits the context transferred between them, preserves attributed outputs, and brings both results to one human review. Raltic verifies both as bridge runtimes and can place them in the same workspace.
Read the answerRaltic does not automatically mirror or upload a repository for bridge-hosted agents. Claude Code or Codex reads the repository on the machine running the local runtime. Raltic receives messages, artifacts, and run status deliberately posted into a workflow room. The AI CLI may separately send code or context to its model provider under that provider's terms.
Read the answerA bridge runtime is a local AI agent process connected to a shared orchestration platform through an authenticated daemon. The local runtime receives room work, uses tools and files available on its machine, and posts selected messages, artifacts, and status back to the team workspace. In Raltic, Claude Code and OpenAI Codex are verified bridge runtimes.
Read the answerTeams should review AI agent output against the original brief and acceptance criteria, inspect supporting and conflicting evidence, validate high-impact claims or changes in the real environment, check uncertainty and affected scope, and record whether the output was approved, rejected, or revised. A completed run is not the same as accepted work.
Read the answerHuman-in-the-loop agent orchestration coordinates agents, tools, tasks, and handoffs while placing qualified people at meaningful decision points. Agents perform bounded preparation and propose results; humans inspect evidence, resolve ambiguity, authorize consequential transitions, and remain able to stop or revise the workflow.
Read the answerAI agents need a shared workspace when several people or runtimes must coordinate around the same recurring work. The workspace should preserve a common brief, attributed outputs, tasks, artifacts, review state, and the final decision. A solo, one-off task may not need one; team workflows with handoffs and accountability usually do.
Read the answerAn AI agent orchestration platform coordinates how one or more agents receive context, use tools, share state, hand work to other agents or people, recover from failure, and reach a reviewed outcome. The category includes different layers: developer frameworks, runtimes, automation builders, cloud lifecycle platforms, and team workflow products. A useful evaluation starts by identifying which layer the team actually needs.
Read the answerAn AI agent framework is a developer library for defining models, tools, state, and control flow. An AI agent platform supplies more of the operating environment, such as managed runtime, deployment, persistence, observability, evaluation, identity, integrations, or a team-facing workflow surface. Some vendors offer both. The practical difference is how much application and operational work your team still owns.
Read the answerAI agents hand off tasks by transferring a bounded task package to another agent or person. The package should include the objective, completed work, source evidence, artifacts, constraints, unresolved questions, uncertainty, current state, and the next acceptance condition. A message such as 'continue this' is not a reliable handoff because it hides what the next participant needs to trust and finish the work.
Read the answerAn AI agent audit trail should record the workflow brief, participants and runtimes, relevant inputs, source evidence, tool events, task and run state, posted artifacts, errors, reviewer feedback, and final decision. It should distinguish execution completion from human approval and should not claim access to hidden model reasoning. The record must be useful for investigation and recovery, not merely verbose.
Read the answerAI agents should require human approval before actions that are high-impact, hard to reverse, legally or financially consequential, customer-facing, permission-changing, or based on uncertain evidence. Low-risk preparation such as drafting, classifying, and summarizing can often proceed without approval. The reviewer must have relevant evidence, enough time, and real authority to reject or change the next step.
Read the answerCompare AI agent platforms across nine dimensions: the layer they own, runtime model, context and memory, tool permissions, orchestration control, human review, observability, failure recovery, and total operating cost. Use one real workflow and the same acceptance criteria for every product. A framework, automation builder, cloud platform, and shared team workspace should not receive the same scorecard because they solve different parts of the stack.
Read the answerAn AI agent builder is a visual or low-code development environment for configuring an agent's models, instructions, tools, knowledge, state, routes, tests, and delivery surface. Builders reduce the amount of application code needed for common patterns, but the team still owns permissions, failure handling, evaluation, deployment, and ongoing operation.
Read the answerAn AI agent SDK is a code library that helps developers build agent applications with reusable primitives such as agents, model clients, tools, run loops, state, sessions, handoffs, guardrails, tracing, and human interruptions. The SDK accelerates application logic; it does not automatically provide the finished user interface, business workflow, deployment, or operating team.
Read the answerAI agent observability is the ability to reconstruct and analyze what happened during an agent run across model calls, retrieval, tools, handoffs, state changes, latency, cost, errors, and final outcomes. It depends on structured traces and metadata, not only application logs. Evaluation adds quality judgments to that observed behavior.
Read the answerAn AI agent evaluation measures whether an agent completed a task well and whether its path was acceptable. It can score the final output, evidence, tool choice and arguments, intermediate trajectory, policy compliance, latency, cost, and human correction. Reliable evaluation uses reviewed cases, explicit criteria, repeatable runs, and more than one evaluator type.
Read the answerMulti-agent orchestration coordinates which specialist agents run, in what order or in parallel, what context and tools each receives, how state and outputs move between them, when control is handed off, how failures recover, and where a human intervenes. It can be model-directed, code-directed, or a hybrid of both.
Read the answerAn AI agent control plane is the management layer used to register, configure, route, secure, observe, evaluate, and govern many agents or runtimes. It commonly manages agent identities, policies, deployment versions, sessions, gateways, telemetry, budgets, and lifecycle. The data plane performs the actual model and tool work; the control plane manages how that work is allowed and operated.
Read the answerNeed the full method?
The answer library handles narrow questions. The blog covers broader operating methods, and feature pages define exactly what Raltic ships today.