DigiEdze AI · AgentOps
Observe Every Agent & Control Every Action
An agent that acts on enterprise systems needs the operational rigour you give production infrastructure: full traces, enforced policy, measured success, attributed cost and an audit trail that stands up to review.
- agent tracing
- tool-call observability
- agent evaluation
- policy enforcement
- agent cost
- agent health
- audit trails
Reference architecture
- 06Agent fleet
- 05Trace collection
- 04Policy engine — allow · deny · approve · escalate
- 03Evaluation & scoring
- 02Cost attribution
- 01Audit store & dashboards
The problem
What Happens When This Misleads?
When an agent produces a wrong outcome, nobody can reconstruct why it chose that path.
Agent permissions accumulate quietly until the blast radius is unknown.
Agent cost is invisible until an unexpected invoice arrives.
What we build
AgentOps Capability Grid
End-to-end agent tracing
Every plan, reasoning step, tool call, result and retry captured as one linked trace.
Tool-call observability
Which tools, with what arguments, how often, at what latency, with what failure rate.
Policy enforcement
Allow, deny, require approval or escalate — evaluated before every action and recorded with its rationale.
Agent evaluation in production
Trajectory and outcome scoring on live traffic, not only in the test suite.
Cost per agent and per task
Token, tool and compute cost attributed to the agent, the workflow and the business owner.
Audit trails
Immutable, queryable records of what each agent did, under whose authority, with what effect.
How it works
Build AI agents and LLM apps around your business needs by integrating 100+ frameworks and practical approaches tailored to your environment through the following technology stack.
Technology
- OpenTelemetry-based tracing
- Policy-as-code engines
- Evaluation frameworks
- Cost telemetry
- Immutable audit stores
Use Cases
Our Definitive Approach
BFSI
Every autonomous action carries an approval chain and a retained audit record.
IT Operations
Remediation agents monitored for success rate and rollback frequency.
Security
Containment actions gated by policy with mandatory human approval above a severity threshold.
Outcomes
What We Measure
Connect to your enterprise applications, including ERP, CRM and knowledge repositories, through controlled tool access. AgentOps brings operational reliability and innovation to your enterprise.
- Agent success rate
- Human escalation rate
- Policy violation rate
- Cost per completed task
Questions
AgentOps, Answered Directly
What separates AgentOps from application monitoring?
Application monitoring answers whether the service responded. AgentOps answers whether the agent did the right thing: was the plan sound, were the tools appropriate, was the action permitted, was the outcome verified, and what did it cost. Those questions need trajectory-level data that request metrics do not carry.
When should AgentOps be introduced?
Before the first agent reaches production. Retrofitting tracing and policy onto a live fleet means operating blind in the interval, which is precisely when the expensive mistakes happen.
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Design Your AgentOps Layer
We bring durable orchestration and governance to every enterprise. Scale your agentic automation with us today.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT