See what your agents did, what purpose applied, and when they needed a human.
As AI agents move from answering questions to doing real work, organizations need to know what happened. TELOS shows what an agent did, what assigned purpose applied, what the resulting record shows, when it escalated, and what evidence remains afterward.
Agents are doing real work. The answers lag behind.
Approvals do not scale to the volume of consequential actions. Logs record calls, not whether the work matched its purpose. TELOS sits alongside the agent and keeps the record a human can check.
Six steps, start to finish.
TELOS does not run your agents. It makes the work they do observable against the specification a human gave them, and it delivers tamper-evident evidence of what the agent did, when it did it, and how.
A human specifies the work
Purpose, escalation conditions, and evidence expectations. The specification is the human's, in writing, before the agent moves.
The agent performs the work
Inside the declared context, on whatever stack you already run. TELOS is not a model and not an agent framework.
TELOS observes runtime action
On instrumented paths, consequential actions are recorded against the specification: the action, the purpose it was measured against, per-dimension scores, and a review verdict.
Human review signals are surfaced
Consequential calls become visible to your people, and the human decision lands in the record.
The evidence preserves what happened
On instrumented paths, observed actions produce durable, tamper-evident evidence: records with an integrity hash anyone can recheck offline, and no raw arguments echoed.
Teams review purpose discipline
The accumulated evidence supports human review of how observed actions compared with assigned purpose over time. The work leaves evidence.
Three rooms, one answer.
Teams deploying agents
Agency owners, RevOps leads, SMB operators. You want to see what the agent did and show a human stayed in authority, without clicking approve on everything.
People instrumenting workflows
Developers and automation consultants. You want evidence and observability added to real agentic workflows without rebuilding the stack. The SDK is free for exactly this.
Compliance and risk stakeholders
Advisors, risk managers, insurers. You want an evidence surface to exist before consequential AI workflows become a liability question. That evidence is the surface.
What works today, what is coming.
A company selling evidence should be the first to concede its early state. Here is ours, plainly.
- Runtime observation against a declared purpose on instrumented paths
- Per-action records with an integrity hash anyone can recheck offline
- Human-review signals recorded into the evidence
- A free SDK to instrument one workflow
- Download-and-reproduce capsule for the published run
- Signed receipts (records are integrity-hashed and unsigned today)
- Broader coverage beyond instrumented paths
Start with one workflow.
Pick one consequential workflow. Define the human purpose. Run your agent. Review the evidence. One narrow workflow, not an enterprise rollout.