01 Automatic Pipeline Creating 02 Pipeline and Agents 03 Creating New Agents 04 Memory and Monitoring
Platform · 04

Watch every decision.
Let the system learn.

Revula logs every agent action in real time — and uses that history to build memory that makes every future run faster, smarter, and more accurate.

Live monitoring

Full visibility into every pipeline run

Every time a pipeline runs, Revula logs every agent — what it received, what it decided, how long it took, and how many tokens it used. No black boxes.

Revula · Monitoring Live
247
Total runs
241
Successful
6
Errors
1.4M
Tokens in
186k
Tokens out
TimeLevelPipelineAgentEventTokens inDuration
14:32:07 SUCCESS Invoice PDF Flow PO Matcher agent_output 4,821 2.3s
14:32:04 INFO Invoice PDF Flow PDF Extractor agent_input 1,104 —
14:32:01 INFO Invoice PDF Flow Email Trigger pipeline_started — —
14:28:55 WARNING Lead Qualifier Router Agent validation_failure 612 0.8s
14:27:12 SUCCESS Lead Qualifier Enrichment Agent agent_output 3,240 4.1s
Persistent memory

Revula remembers.
Agents improve.

Every pipeline run feeds Revula's memory layer. Over time, agents build an institutional understanding of your business — your patterns, your preferences, your exceptions.

Memory isn't a log — it's a living knowledge base that's injected into every agent's context, making each run smarter than the last.

Static Memory
Fixed knowledge — agent instructions, SOPs, user feedback rules — that doesn't change between runs.
Vectorized Memory
Run journals and reference documents stored as embeddings — semantically searchable, retrieved by relevance at runtime.
Structured Memory
Pipeline flow descriptions and operational state — injected into every agent so it always knows the full context of the workflow it's part of.
Memory layer · live Active
LLM agent Memory Manager VFS index STA TIC mem VEC TOR mem STR UCT mem
How the system improves

Each run makes the next one better

01 — Learn from outcomes
Every run writes to memory
Agent outputs, decisions, and errors are stored as run journals. On the next execution, agents read relevant past runs and use them to inform their reasoning.
02 — Incorporate feedback
Your corrections become rules
When you flag an agent decision as wrong, Revula writes a feedback entry to static memory. That correction is applied to every future run of that agent — permanently.
03 — Adapt to your patterns
The system learns how you operate
Over time, Revula builds a model of your team's patterns — what you approve, what you flag, how you handle exceptions. The result is automation that fits your business, not a generic template.

See memory in action

Ask us to show you a real monitoring report from a live pipeline run during our pilot call.

Contact us