[ AI Governance and AgenticOps ]

Run AI Agents With the Controls Auditors Ask For

We put policies, evaluations, approvals and monitoring around the AI agents you already run or plan to launch. You get evidence of control that maps to the EU AI Act, NIST AI RMF and ISO 42001.

Book a Governance AssessmentSee the Toolkit
Certified security management
ISO 27001
Certified security management
Defined, repeatable delivery processes
CMMI L3
Defined, repeatable delivery processes
Frameworks mapped: EU AI Act, NIST, ISO 42001
3
Frameworks mapped: EU AI Act, NIST, ISO 42001
Weeks to a working governance baseline
4 to 8
Weeks to a working governance baseline
Leads talking in a meeting area

Controls mapped to

Framework 1EU AI ActRisk tiers and obligations per system
Framework 2NIST AI RMFGovern, map, measure and manage
Framework 3ISO/IEC 42001AI management system requirements
[ When teams call us ]

When Teams Bring Us In for Governance

Most teams call once agents start taking real actions, or when a customer, regulator or board asks how AI risk is managed.

  1. Open office with teams working at rows of computers

    AI inventory and risk classification

    Find every model, agent and AI feature in use, record its owner, data and purpose, and classify its risk level under the EU AI Act categories.

    One registry that answers what AI you run and why

  2. Developer reviewing code at his desk

    Release gates for agents

    Evaluation suites and red team runs wired into CI, so a new prompt, model or tool cannot ship until it clears agreed thresholds.

    Regressions are caught before customers see them

  3. Operations engineer watching a dashboard on his monitor

    Production monitoring

    Live tracking of task success, tool errors, policy violations, drift and cost per run, with alerts when an agent behaves out of pattern.

    Problems surface in hours instead of support tickets

  4. Manager signing off a request on a tablet

    Human approval design

    Decide which agent actions need a human, build the approval queues and record who approved what, with timing and evidence.

    Autonomy grows only where it has been earned

  5. Team presenting a review in a meeting room

    Enterprise and customer due diligence

    Prepare the AI sections of security questionnaires, model cards and policy documents that enterprise buyers now request.

    AI questions stop slowing down enterprise deals

  6. Reviewer checking figures on a tablet beside a calculator

    Regulatory readiness

    Gap analysis against the EU AI Act, NIST AI RMF and ISO 42001, with a prioritized plan and the technical controls to close the gaps.

    A clear path to audit instead of a scramble

[ Architecture ]

The Control Plane Around Your Agents

Governance sits beside your agents rather than inside each one, so the same controls apply whichever framework or model a team uses.

  1. Governance portal

    Where owners, risk and compliance teams see and decide.

    • AI registry
    • Risk register
    • Approval inbox
    • Compliance reports

  2. Policy enforcement

    Checks applied to every request, action and response.

    • Policy engine
    • Action limits
    • Input and output filters
    • Kill switch

  3. Agent runtime

    The agents under governance, on any framework.

    • LangGraph
    • CrewAI
    • OpenAI Agents SDK
    • kagent

  4. Evaluation and testing

    Scores behavior before release and samples it after.

    • Eval suites
    • Red team harness
    • LLM judges
    • Regression history

  5. Telemetry and evidence

    Collects what happened so it can be proven later.

    • Traces
    • Audit log
    • Cost metering
    • Model cards

The same controls apply whichever framework or model a team uses for its agents.

[ The toolkit ]

The AgenticOps Toolkit We Set Up

Governance only works when it is built into how agents are developed and run. These are the pieces we implement with your team.

  • 01

    Policies as code

    Written AI policies turned into enforceable checks: action limits, data rules and banned behaviors evaluated on every run.

  • 02

    Evaluation pipelines

    Task-specific test sets, LLM-as-judge scoring calibrated against human labels, and pass rate thresholds per release.

  • 03

    Red teaming

    Structured adversarial testing for jailbreaks, prompt injection, tool misuse and harmful outputs, repeated after each major change.

  • 04

    Observability

    Traces of every plan, tool call and output with OpenTelemetry, plus dashboards for success rate, latency and policy hits.

  • 05

    Approval workflows

    Risk-tiered human review for sensitive actions, with service levels and escalation when approvers do not respond.

  • 06

    Agent identity and access

    Each agent gets its own identity, scoped credentials and permission reviews, just like a human employee.

  • 07

    Cost controls

    Budgets per agent and per run, model routing rules and alerts when spend moves away from forecast.

  • 08

    Audit evidence

    Immutable logs, model cards and release records collected automatically, ready for internal audit or a certification body.

[ Standards we align with ]

Mapped to the Frameworks Your Auditors Use

We prepare you for ISO 42001 and EU AI Act obligations with gap analysis and controls. Formal certification is issued by an accredited body.

  • Standard

    EU AI Act

    Classify each AI system by risk tier, record its purpose and data, and plan the obligations that apply to high-risk uses.

  • Standard

    NIST AI RMF

    Map your controls to the govern, map, measure and manage functions so risk work has a common structure.

  • Standard

    ISO/IEC 42001

    Gap analysis against the AI management system standard, with the policies and records an auditor will ask for.

  • Standard

    OWASP Top 10 for LLMs

    Test agents for prompt injection, insecure output handling, excessive agency and the other listed risks.

  • Standard

    MITRE ATLAS

    Use the adversarial tactics catalog to plan red team scenarios that match how attackers target AI systems.

Team reviewing documents together at a desk by the window

Two-week assessment

Find Out Where Your Agents Stand Before Someone Asks

We inventory your AI systems, test the riskiest ones and hand you a prioritized gap list against the EU AI Act, NIST AI RMF and ISO 42001.

Book the Assessment
[ Governance in practice ]

Governance and Compliance Work in Practice

AI products where accuracy, traceability and regulated data shaped the design.

Governance, risk & compliance

Sannos AI

The challenge
Each engagement began in a fresh spreadsheet, controls were re-assessed for every framework, and audit trails were stitched together at the end.
What changed
Advisors pick a framework, reuse evidence across regulations, review AI-drafted ratings and export a defensible report in a fraction of the time.
Read the case study

Results after launch

Framework setup
1-2 daysto deploy a new framework, down from several weeks
Review effort
-60%fewer analyst hours per control assessment
Reuse
4xaverage reuse of each piece of evidence across frameworks
Frameworks
15+regulations and standards mapped in the library
[ Principles ]

Principles Behind Our Governance Work

We aim for controls that teams actually follow, not documents that sit in a shared drive.

  • Proportional to risk

    A marketing copy assistant and a payments agent need different controls. We tier requirements so low-risk tools are not buried in process.

  • Measured, not asserted

    Every control produces evidence: eval scores, red team findings, approval records and trace samples you can show an auditor.

  • Privacy by default

    Traces and eval sets are masked for personal data, stored in your region and kept only as long as your retention policy allows.

  • Readiness, not certification

    We prepare you for ISO 42001 and EU AI Act obligations with gap analysis and controls. Formal certification is issued by an accredited body.

[ Client voices ]

Teams That Trust Us With Their Controls

Rated 4.9/5 on average across the platforms clients review us on.

  • Clutch5.0
  • GoodFirms5.0
  • Google5.0
  • Upwork4.9
I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.
DKDaniel KwekaCEO, Darius Digital
[ Industries ]

Governance Built for Your Sector

The frameworks are shared. The risk tiers and evidence each sector needs are not.

Finance team meeting around a long table

FinTech

Agents that touch payments, credit or customer data get the tightest approval tiers and full audit trails.

  • Model risk documentation
  • Approval queues for money movement
  • Fair lending and bias checks
  • Evidence for regulators
More on this industry
[ Technology stack ]

Frameworks and Tools in Our Governance Stack

We work with open standards and tools you can keep running after we leave, and fit into platforms you already use.

Standards and frameworks

  • EU AI Act
  • NIST AI RMF
  • ISO/IEC 42001
  • OWASP Top 10 for LLMs
  • MITRE ATLAS

Evals and red teaming

  • Promptfoo
  • DeepEval
  • Ragas
  • Garak
  • PyRIT

Observability

  • LangSmith
  • Langfuse
  • Arize Phoenix
  • OpenTelemetry
  • Datadog

Guardrails

  • NeMo Guardrails
  • Guardrails AI
  • Llama Guard
  • Open Policy Agent

Agent runtimes

  • kagent
  • Managed Deep Agents
  • LangGraph
  • Vercel Eve
[ How we deliver ]

How We Set Up AgenticOps

We start with the agents carrying the most risk, prove the controls there, then roll them out across teams.

Team gathered in a meeting room

  1. 01

    1 to 2 weeks

    Discovery and inventory

    Catalog AI systems, interview owners, classify risk and compare current practice against your target frameworks.

    • AI registry
    • Gap analysis
    • Fixed quote

  2. 02

    1 to 2 weeks

    Control design

    Write policies, define approval tiers, choose metrics and thresholds, and agree who owns each control.

    • Policy set
    • Control matrix

  3. 03

    3 to 6 weeks

    Implementation

    Build eval pipelines, red team harness, tracing, guardrails and dashboards on the highest-risk agents first.

    • Release gates
    • Monitoring dashboards
    • Runbooks

  4. 04

    Quarterly

    Continuous assurance

    Recurring red team rounds, eval set refreshes, access reviews and an evidence pack for audit and leadership.

    • Assurance report
    • Updated risk register
[ Pricing ]

What AI Governance and AgenticOps Costs

Indicative starting prices. Scope depends on how many AI systems you run and which frameworks apply.

  • Governance assessment

    Inventory, risk classification and a gap analysis against one framework.

    From

    $3,000

    2 weeks

    • AI system inventory
    • Risk classification
    • Framework gap analysis
    • Prioritized roadmap
    Get a quote

  • AgenticOps baseline

    Evals, red teaming, monitoring and approvals for up to three production agents.

    From

    $12,000

    4 to 8 weeks

    • CI release gates
    • Red team harness
    • Tracing and dashboards
    • Approval workflows
    • 30 days of support
    Get a quote

  • Enterprise governance program

    A control plane across business units with continuous assurance.

    From

    $30,000

    3 to 6 months

    • Governance portal and registry
    • Policy engine across agents
    • ISO 42001 readiness pack
    • Quarterly red team rounds
    Get a quote
[ FAQ ]

AgenticOps Questions

Still deciding? A 30-minute call usually answers the rest.

Ask Us Directly

  • AgenticOps is the practice of running AI agents in production the way you run any critical service: with versioned releases, automated tests, monitoring, access control and incident response. It extends MLOps and LLMOps to systems that take actions, not just generate text.

  • It can. The Act applies to providers and deployers whose AI systems are used in the EU, regardless of where the company is based. We help you classify each system, but for a legal opinion you should also consult your counsel.

  • No. Certification is issued only by accredited certification bodies. We prepare you for it by setting up the AI management system, controls and evidence the auditor will look for, and can support you during the audit.

  • LLMOps focuses on deploying and tuning models and prompts. AgenticOps adds controls for actions: what an agent is allowed to do, who approves it, how its tool use is tested and how you prove all of that later.

  • We run structured attacks such as jailbreaks, indirect prompt injection through documents and tools, privilege escalation through tool calls and data exfiltration attempts. Findings are ranked by severity with a reproduction and a fix, then retested.

  • No. The controls sit around your agents through tracing, gateways and CI checks, so they work with LangGraph, CrewAI, OpenAI Agents SDK, custom code or hosted agents.

  • Typical metrics are task success rate, eval pass rate, tool error rate, policy violation count, human override rate, latency and cost per run. We agree on targets per agent and alert when they drift.

  • An inventory and gap analysis takes about two weeks. A working baseline with release gates, monitoring and audit logs on your key agents usually takes four to eight weeks.

  • Done well, it speeds them up. Automated gates replace manual sign-offs for low-risk changes, and teams ship with more confidence because regressions are caught early.

[ Related services ]

[ Contact ]

Let's build your next product together

Book a free strategy call and leave with a clear plan and estimate. No commitment.

  1. 01Pick a time that suits you
  2. 0230 minutes on scope, stack, timeline and budget
  3. 03Fixed-price proposal, NDA on request
Prefer email? contact@sajaltech.com