Definition
Agent session
An agent session is a single bounded run of an AI agent toward a goal — its sequence of observations, tool calls, and results, with an attached identity, capability scope, budget, and audit record. Treating a run as a first-class session is what makes an agent's behavior governable and reviewable: it has a beginning, an accountable actor, and a transcript you can later distill or inspect.
Key takeaways
- An agent session is a single bounded run of an agent — its observations, tool calls, and results — with identity, scope, budget, and audit.
- Modeling the run as a session gives governance a concrete unit to attach control and review to.
- A finished session is reusable — it can be distilled into a named skill or inspected to explain a decision.
- Planoda runs agents as tenant-scoped sessions, governed by the broker and distillable into skills.
An agent doesn't act in the abstract; it acts within a run. Modeling that run as a session gives governance something concrete to attach to: the session carries which principal it acts as, which tools it's scoped to, how much budget it may spend, and a transcript of every step it took. When something needs review, the session is the unit — you read its trail, not a scattering of disconnected log lines.
Sessions also make agent work reusable. Because a finished session is a complete record of how a task was accomplished, it can be distilled into a named skill for next time, or inspected to understand why an agent made a particular call. The session is simultaneously the boundary for control during the run and the artifact for learning after it.
Planoda's agents run as tenant-scoped sessions whose transcripts can be distilled into reusable skills, whose tool calls flow through the propose/approve broker, and whose consequential actions land in the shared audit trail — so each run is governed while it happens and reviewable once it's done.
Related terms
- AI AgentAn AI agent is a software system that uses a large language model to pursue a goal across multiple steps — reading context, choosing tools, and taking actions — rather than answering a single prompt. In a work platform, agents triage issues, draft updates, and execute multi-step tasks as autonomous teammates, bounded by the permissions and approvals their operators set.
- Skill distillationSkill distillation is the process of turning a finished agent session into a reusable, named skill: a model reads the transcript and extracts a structured capability — a description, a system prompt, and an allowed set of tools — so a successful one-off task becomes a repeatable behavior. It converts tribal prompt knowledge into a governed, shareable asset.
- Agent broker (propose/approve broker)An agent broker is the runtime component that sits between an AI agent and the actions it wants to take, deciding per call whether to execute inline, require human approval, or refuse. It is where propose/approve governance is actually enforced — the gate is code in the request path, not a guideline in a prompt — so an agent literally cannot run a destructive tool without passing through it.
- Capability scopingCapability scoping is restricting an AI agent to a defined set of tools and permissions for a given task or session, so it can only do what that role requires — and nothing more. It applies least-privilege to agents: a triage agent gets triage tools, a drafting agent gets drafting tools, and neither can reach destructive operations outside its scope, whatever a prompt might ask of it.
- Agent audit logAn agent audit log is an immutable, append-only record of everything an AI agent did and proposed — each action, who or what approved it, when, and against which records — written to the same trail as human actions. It turns 'the agent did something' into an accountable, replayable history, and is what makes governed autonomy verifiable rather than merely promised.