Everything your agents touch, governed.

TraceMem checks every action your AI agents take before it happens, holds the ones a person should approve, and keeps the record that answers for each one. Self-hosted, inside your own network.

Book a demo See what it does

Your agents already have access. Nothing decides what they may do with it.

In financial services, healthcare, law enforcement, critical infrastructure and the public sector, agents are already reading case files, moving money, changing records and calling systems a regulator will audit. They reach them through service accounts with standing access, under no verified identity and no per-action authority.

The controls you have were written for people and for applications. They sit at the edge of systems the agent is already inside, they never see a prompt, and they cannot hold one action and ask a person first. A policy that cannot refuse at the moment of the action is documentation.

Put your agents to work. Keep every decision yours.

Every action is checked before it happens, so agents can reach the systems that matter while your people stay in charge of what they do there.

See how it all fits together

Your agents don’t change. Their authority does.

Without TraceMem

Every agent finds its own way in

Each agent, framework and integration arrives with its own credentials, its own connection and its own idea of what it may do.

Approval rules live inside each agent

Every team decides for itself what needs a human. The rules drift, they contradict each other, and nobody can state the real policy.

The answer takes days to assemble

Identity sits in one system, the prompt in another, the approval in a chat thread. Reconstructing a single action is a project of its own.

With TraceMem

One governed path in

Model calls, data reads and tool calls all arrive the same way, so a control covers every one of them rather than the ones a team remembered.

One set of rules, applied everywhere

Rules are written once and versioned. Needing a human means the same thing for every agent, and the request reaches someone who can answer it.

The answer is already written down

Who it was for, what was checked, who approved it and what changed sit on one record, because they all happened in the same request.

TraceMem runs inside your network.

It deploys into your environment and your team operates it. Your data and your traces stay on your side of the boundary, and the only traffic that leaves is traffic your own policy let out.

A secure building seen through glass: turnstiles at the entrance, a hall of server racks, a row of fan-cooled racks, and a vault beside an archive of sealed boxes.

Your network

Every governed action is recorded in this much detail.

A trace carries the detection layers that ran, the products and purposes touched, the policy version and hash that applied, who approved what, and what changed as a result.

This one decision produced seventy-three events, each written before the step it records was allowed to proceed. Nothing is assembled from logs afterwards, and the chain makes a later edit detectable.

An evidence counter: bagged documents, a phone, a drive and a key laid out in a line, each with its own numbered tag, and a gloved hand placing a photograph at the near end. — seq 000–07210 of 73 events itemised

TRACEMEM

Decision receipt

tracemem.trace.v3

decision_id: DENV_01M1YQV8HAA6EVQ4RYPN7SR6N0
intent: credit_limit_increase
actor: m.lindqvist@northgate.example
automation_mode: propose
status: committed

seq · eventtime

000–015 · 16 events
016pii_detection_hit20:09:43
PII detected in the inbound prompt (IBAN)
layer
stage_1_pattern
pii_type
iban
confidence
0.95
017pii_policy_decided20:09:43
Configured policy applied to the detected spans: tokenize
direction
input
mode
tokenize
tokens_minted_count
4
018–020 · 3 events
021pii_obfuscation_applied20:09:43
IBAN replaced with a reversible pointer token
pii_type
iban
strategy
pointer
token_ref
tracemem_token:tok_42OXOEB3ER2M
022–053 · 32 events
054read20:09:47
Read executed by TraceMem on the agent's behalf
product
bank_customer_profile_read
purpose
credit_limit_assessment
entities
CUST_1001
055–057 · 3 events
058read20:09:48
Second read, under a different declared purpose
product
bank_risk_signals_read
purpose
financial_crime_gating
059–061 · 3 events
062policy_eval20:09:48
Published policy evaluated to require_exception
policy_id
credit_limit_increase_v1
policy_hash
280bc423eb4bae18
result
require_exception
reason_code
INCREASE_NEEDS_APPROVAL
063approval_requested20:09:48
Exception routed to the configured approval channel
channel
slack
approval_route_id
credit_risk_approvals
064approval20:10:04
Exception resolved by the named approver
approved
true
approver
d.moriarty <U0B75QUM1DK>
comment
approved
065–066 · 2 events
067write_completed20:10:04
Write executed on the source system
product
bank_credit_facilities_update_limit
operation
update
duration_ms
86
068–071 · 4 events
072outcome20:10:05
Envelope finalized as committed
outcome
human_allow

events recorded: 73
events itemised: 10
Outcome: human_allow

scheme: decisiondb.envelope.v1
algorithm: SHA-256
content_hash: e69b43108070d46cbb26b56afeefcce8

chain_hash f9827e632ab8a3ca73b5db98855605eb

Each line written before its step proceeded. Anyone you hand it to can verify it.

73 events
Chain verified

You already have the agents. This is the part that makes them defensible.

Tell us what your agents touch today. We will walk through what a governed path around them looks like in your environment.

Book a meetingSkip the form. Pick a time in our calendar and bring the agent you are most worried about.

Talk to us now

45days, free

Free for 45 days on our Docker-based trial image, inside your own network. When you are ready, it upgrades to the full enterprise product.

Forward-deployed engineers, ours or our partners', can set it up in days and help you define policies and integrations. This may be charged separately.