1 · The agent says it
“The Global Equity Index Fund has returned an average of 10.5% a year over the last ten years.”
Sounds right. Is it?
Reasoning Markup Language (RML)
See why your agent decided, not just what it did.
AISquare records every run in RML: the claims the agent made, the evidence behind each one, and the assumptions it filled in.
The problem
A transcript tells you what was said.
It doesn't tell you why.
The why disappears
The evidence and shortcuts behind an answer are gone the moment it appears.
Silent assumptions
Agents fill gaps you never see, until one of them is wrong in production.
No answer for auditors
When someone asks why the agent did that, all you have is a wall of transcript.
The trust loop
You can't trust an agent you don't understand.
Trust is earned in a loop. Understand why the agent decided, prevent the bad action next time, and fix what already happened. It starts with seeing the reasoning.
You are here
Understand
See why it decided, in RML.
Prevent
Stop a bad action before it runs.
Fix
Correct a decision where it happened.
Remember
Carry the fix to every run.
Here's what understanding looks like in practice.
How it works
One language for how agents reason.
Every model explains itself differently. RML gives every agent one format for its reasoning, so other agents, reviewers, auditors and your own tools all read it the same way.
ANY AGENT WRITES IT
OpenAI agent
Claude agent
Gemini agent
LangChain
Agno
Your own agent
RML record
1Run header · agent, model, steps
2Claims
3Evidence, with confidence
4Assumptions
5Inference chain
6Unbacked claims flagged
ANYONE CAN READ IT
Other agents
Reuse past reasoning as precedent.
Reviewers
Read it in plain language.
Auditors
Find it in the signed record.
Your tools
Pull it over the API.
Built on OpenTelemetry, so it fits the tracing you already run.
We're working with the Trust & Safety Institute to publish RML as an open standard.
The catch
The claim sounded right. Nothing backed it.
Agents state numbers with total confidence. Without the reasoning, you can't tell a real figure from a made-up one.
2 · AISquare checks what backs it
Evidence for this claim
Fund factsheet · retrieved
covers 2019 to 2024 only
Performance database · never queried
No source in this run says 10.5%
Unbacked claim3 · You make it a rule
Every return figure must cite a fund source.
Every agent checks it from the next run on.
See the Policy EngineIllustrative example.
What else RML catches
Three mistakes traces don't show you.
Each one is pinned to the exact step that caused it, so you fix the cause, not the symptom.
RUN 4821 · CLAIMS AGENTHallucination risk · High
Unbacked claim
Quoted a 10.5% average return.
No source retrieved in this run contains that figure.
Skipped step
Never called the eligibility lookup.
The instructions require it before any decision. The agent decided without it.
Error that spread
A wrong customer match in step 2 flowed into the decision.
The lookup matched the wrong record, and every later step built on it.
Illustrative example.
Reasoning markup (RML)
Claims, evidence, assumptions.
Every RML record breaks a run into these parts, step by step.
Claim
The refund is within policy limits.
EVIDENCE · 94%
Order #4821, $39, placed 6 days ago
EVIDENCE · 91%
30-day refund window
ASSUMPTION · 0.72
Customer tier resolved to standard · inferred, not confirmed
- 1Order total $39 is under the $50 auto-approve ceiling.
- 2Placed 6 days ago, inside the 30-day window.
- 3No prior refund on this order.
- 4Therefore auto-approvable without review.
Claims
What the agent asserted and acted on.
Evidence
What each claim rests on, each with a confidence score.
Assumptions
The gaps the agent filled in for itself.
86%
Extraction confidence
How faithfully the reasoning was captured. Low means read the raw trace, not the summary.
The reasoning goes into the signed record for every run. See Audit
Observability and reasoning
Everything you see on every run.
This is the run view your engineers and reviewers open. Here's what every part of it tells you.
I'm 32, medium-high risk tolerance, 15-year horizon...
21.1s · 5 spansPrompt injection: 1FlowGraph
6 steps · click any blockINTAKE
Customer request received
AGENT3.95s
FinanceGuidanceAdvisor
LLM1.32s
gpt-4o-mini
TOOL CALL2ms
Recommend Specific Product
LLM2.57s
gpt-4o-mini
DECISION
DecisionIMPROVED
Cost across providers2 cheaper options7 pricier optionsactual $0.0006
● SuccessDuration 21.1sCost $0.0006Tokens 2,318
DetailsTraceRMLPoliciesAttestations
By LLM nodeWhole run
Analysis confidence62%
5
Reasoning
5
Assumptions
8
Claims
1
Evidence used
3
Unbacked
1 · AGENTFinanceGuidanceAdvisorno evidence
2 · AGENTFinanceGuidanceAdvisor2 unbacked
REASONED
The user has a medium-high risk tolerance and a long horizon.
↓ concluded A global equity fund is appropriate.
ASSUMED
A 15-year horizon is long enough for global equity exposure.
CLAIMED
I recommend a specific ESG global fund.
UNBACKEDThis fund is ESG-screened and low-cost.
DERIVEDPROMPT INJECTION
1 attempt to override the agent was detected and reviewed.
Delimiter ConfusionHIGH
1
Every step, in order
Each LLM call, tool call and agent hand-off as it ran.
2
Latency per step
See which step slowed the run down.
3
Tool calls
What the agent called, with inputs and timing.
4
Fixed before it shipped
Decisions your rules corrected, marked on the run.
5
Cheaper model options
What the same run would cost on other models.
6
Attacks caught
Attempts to override the agent, flagged with the pattern and severity.
7
Reasoning, rules and the signed record
RML sits next to Policies and Attestations on every run.
8
Per step or whole run
Read the reasoning step by step, or for the run.
9
Analysis confidence
How faithfully the reasoning was captured.
10
Claims, assumptions, evidence
Counted for every run, so risk is visible at a glance.
11
Steps with no evidence
Risky steps are marked, so you know where to look.
12
Unbacked claims
Claims with nothing behind them, flagged in red.
See the full list
OBSERVABILITY
Full trace
Every LLM call, tool call, retrieval and decision, in order.
Latency per step
See which step slowed the run down.
Tokens and cost
Per step, per run, per agent, per provider and model.
Errors and status
Failed steps flagged where they happened.
Policy verdicts
Pass or block, with the reason and the rule it cites.
Sensitive data screening
PII, PHI and PCI detected before anything is saved.
Search and filter runs
By agent, status, errors, policy hits, cost or duration.
Prompt injection detection
Override attempts flagged with pattern and severity.
Cost across providers
Cheaper and pricier model options for the same run.
REASONING
Claims
What the agent asserted and acted on, per step.
Evidence with confidence
What each claim rests on, scored item by item.
Assumptions
The gaps the agent filled in on its own.
Inference chain
How it got from evidence to decision, step by step.
Unbacked claims and hallucination risk
Claims with nothing behind them, and a risk level for the run.
Extraction confidence
How faithfully the reasoning was captured.
Plain-English summary
The whole run, explained in a paragraph.
Suggested fixes
Each finding comes with a plain-language fix.
Sent for review
Low-confidence runs can be routed to a person.
Every item is also available over the API. Read the API guide
In the product
One run, read by an engineer or a reviewer.
Engineers follow the steps. Reviewers read what happened in plain English. Same run, same record.
AISquare · Run trace
Flow
Graph7 steps · click any block
INTAKE
Customer request received
LLM
gpt-4o-mini · 802ms
TOOL CALL
Customer Profile · 2ms
LLM
gpt-4o-mini · 1.41s
TOOL CALL
AISquare · Policy Enforcement · 6ms
LLM
gpt-4o-mini · 1.79s
DECISION
Decision: REJECT
Trace overview
Spans
8
Artifacts
12
Duration
15.8s
Cost
$0.0009
Tokens
5,715
NarrativeCustomer account · claim 15.8s
A customer asked to be reimbursed for cataract surgery. The agent pulled the customer profile, checked the claim against the policy, and ran the policy check. The policy has a 24-month waiting period for cataract surgery and only 10 months have passed, so the agent rejected the claim and cited the waiting-period rule.
Policy check · passedDecision · Reject
1
Every step, with latency and cost
2
The policy check is its own step
3
The same run in plain English
4
Same run, same signed record
Both are reading the same RML record.
For developers
Read the reasoning over the API.
request
curl -s -H "X-API-KEY: $EXPLAINABILITY_API_KEY" \
"$EXPLAINABILITY_GATEWAY_URL/v1/studios/$STUDIO_ID/runs/$RUN_ID/rml"response
{
"extraction_confidence": 0.86,
"claims": [
"The refund is within policy limits."
],
"assumptions": [
{
"proposition": "Customer tier resolved to standard",
"depends_on": "tier was inferred, not confirmed"
}
]
}Every run's RML record is one API call away. Prefer tools? The learnings MCP server wraps every read for Claude Code, Cursor and any MCP client. Read the API guide
FAQ
Questions about AI agent explainability and reasoning graphs.
What is RML?
RML, or reasoning markup, is how AISquare structures an agent's thinking on each run: the claims it made, the evidence each claim rests on with a confidence score, the assumptions it filled in, and the inference chain that connects them.
How is RML different from OpenTelemetry?
OpenTelemetry records what happened: the calls, timings and costs of a run. RML adds the reasoning on top: what the agent claimed, what backed each claim and what it assumed. AISquare's SDK is OpenTelemetry-native, so RML fits the tracing you already run.
Is RML open?
We're working with the Trust & Safety Institute to publish RML as an open standard, so any agent or tool can write and read it. Today, every RML record from your agents is yours to read and export over the API.
Isn't this just the model's own reasoning?
No. A model's reasoning is its own account of what it did. RML checks each claim against the evidence the agent actually retrieved and the steps it actually ran, so a confident claim with nothing behind it still gets flagged.
How is a reasoning graph different from a knowledge graph?
A knowledge graph stores what your organization knows. A reasoning graph records how an agent used that knowledge on one specific run: which claims it committed to, what evidence it weighed and what it assumed. They work together, and a knowledge lookup shows up as a step on the reasoning graph.
How do you catch a claim with nothing behind it?
Every claim is linked to the evidence the agent actually retrieved, with a confidence per item. A claim with weak or missing evidence stands out on the run, so a reviewer sees it and can turn it into a rule for every agent.
How accurate is the flagging?
Every analysis carries an extraction confidence, and a low score tells you to read the raw trace. Reviewers can accept or dismiss each finding, and those calls sharpen how each rule is graded over time.
How are assumptions detected?
Assumptions are facts the agent inferred rather than received, like a customer tier it resolved on its own. They carry a lower confidence and are marked separately, so you can challenge them.
What is extraction confidence?
It's how sure AISquare is that it captured the agent's reasoning faithfully. A low score is a prompt to read the raw trace rather than rely on the structured view.
Does capturing reasoning slow my agent down?
No. Reasoning is extracted asynchronously after a run lands, so the agent runs at its normal speed.
Do our traces leave our environment?
Traces go only to your own AISquare workspace, over TLS. PII, PHI and PCI are screened before anything is saved and can be masked. Talk to us if you have stricter residency needs.
Can it catch a step the agent skipped?
Yes. Because every run is recorded step by step, a required tool call that never happened shows up as a gap on the run, next to the decision that was made without it.
Which frameworks and models does it work with?
Any agent connected through the proxy or the SDK, including Anthropic, OpenAI, Azure OpenAI and Gemini models, Agno, LangChain, custom agents, and existing OpenTelemetry setups.
Can I read the reasoning programmatically?
Yes. List runs, fetch the graph and fetch the reasoning over the API, or use the learnings MCP server from Claude Code, Cursor or any MCP client.
Stop trusting outputs you cannot explain.
Start your journey with AISquare
Connect one agent and see its reasoning, assumptions and all, in your own workspace.