Skip to content
Xplore
Forge
Your agents. evaluated[re]traineddeployedcontrolled evaluated
for your environment.

Load your workflows, connectors, policies, and success metrics. Forge builds the benchmark, optimizes the agent, deploys what passes, and monitors for regression — continuously.

50+
connectors
Simulate workloads from your systems and data sources
Simulation layer
30+
evals
Evaluate chains with composable dimensions and domain weights
Eval framework
4
runtimes
[Re]Train with parallel strategies and guardrailed iteration
Training layer
Live
Observe drift, cost, and promotion gates across deployed versions
Operations layer
Evaluate

Know exactly where your agents fail.

Each domain has different priorities. Set weights — safety-critical domains weight safety at 0.30, clinical domains weight dose accuracy at 0.25. Forge scores each dimension separately on your real tasks.

Forge eval chain · support-bot-v3
Tone accuracy
0.89
Knowledge
0.91
Escalation
0.78
Response time
0.94
Safety
0.97
Hallucination
0.03
Weighted 0.85
tone_acc
w = 0.25
knowledge
w = 0.25
escalation
w = 0.20
Node Resolution graph — Supply Chain environment with 33 vertices, 63 relations, 7 data sources
Evaluate

Your agents train on production-grade data.

Neo4j, PostgreSQL, SAP/ERP, OpenSanctions, OSINT — not toy data. 33 vertices, 63 relations across 7 real data sources in a single environment. Your agents train on the same complexity they face in production.

[Re]train

Agents that work beyond their training data.

In-sample optimizes. Out-of-sample verifies. The gap tells you if the agent performs reliably beyond training data. Every iteration measured on tasks from your operational environment.

Forge training — IS/OS/meta fitness over automated iterations with per-category breakdown
Agent structure diff — tools, rules, and instructions changed between iterations
Config diff
Iteration 22 23
trainer: step_by_step · accuracy_focus
+ tool: verify_source_citation
"Cross-check facts against original document"
~ rule: escalation_policy
threshold: 0.4 → 0.6
~ instruction: verification section
added: "Always cite page number"
score: 0.68 → 0.73 +0.05 promoted
[Re]train

Understand why performance improved.

Every iteration commits changes like git. Tools added, rules rewritten, score delta tracked. Not just prompts — the whole agent structure: tools, instructions, policies, configurations.

Deploy

Agents only go live when they pass.

Auto-promote the best, set a threshold, or keep it manual. Candidates sit on a branch until they earn promotion. Every version tracked. Roll back in one click.

Training run configuration — promote policy, trainer strategy, mutation knobs
Agent overview — Logistics v7, 148 runs, 6 versions, performance over time
Production controls · live
Accuracy & Safety enabled
agent: logistics-v7
last 24h: 148 runs · avg: 0.71
⚠ drift: context_adherence 0.78 → 0.61
RAG quality enabled
agent: chatbot-prod
last 24h: 2,341 runs · avg: 0.84
alerts: 0
Cost guard enabled
agent: bi-analyst-v1
last 24h: 412 runs · avg: 0.91
alerts: 0
Cost Over Time — stacked area chart (agent, eval, trainer, cert)
Control

Know when agents degrade. Retrain when they do.

Live certification + drift alerts + cost transparency. Every dollar tracked — agent, eval, trainer, cert. When scores drop, retraining triggers automatically.

  • ·Context adherence, completeness, PII leak, latency SLA, token budget
  • ·Drift alerts — score 0.78 → 0.61 flagged instantly
  • ·Per-cycle cost visibility in the dashboard — totals by agent, eval, trainer, and certification
Solutions

What you can train.

Every agent type. Every domain. Evaluated on the dimensions that matter to your business.

Chatbots
For: Customer support
Train: tone, accuracy, escalation
refusal_rate 0.03
tone_score 0.89
accuracy 0.91
BI Agents
For: Data teams
Train: query accuracy, interpretation
query_correct 0.94
data_citation 0.87
hallucination 0.02
Research Agents
For: Analysts, researchers
Train: source quality, citation, synthesis
source_quality 0.85
citation_f1 0.78
synthesis 0.82
Action Agents
For: Operations, automation
Train: tool use, safety, efficiency
tool_accuracy 0.93
safety_score 0.99
efficiency 0.76