Scoped tools
Each action an agent can take is an explicit, typed tool with least-privilege permissions, rate limits, and input/output validation.
The platform
Everything around the model.
A capable model is table stakes. What turns it into something an enterprise can depend on is the harness, the evaluations, the pipelines, the controls, and the accountability wrapped around it. That's what we build — and what we operate for you.
01 / How we roll out agents
A contained path from workflow to production, provable at every gate.
A disciplined path from a real workflow to a governed agent in production.
No "throw a model at it and hope." Every engagement follows the same lifecycle, so risk is contained and value is provable at each stage.
STAGE 01
Scope & discovery
We shadow the workflow, quantify the cost of the status quo, and define success in numbers.
- Process map, data inventory, and system-of-record access review
- Decision points classified: automate, assist, or escalate to a human
- Baseline metrics and target thresholds agreed before a line of code
STAGE 02
Build on the harness
The agent is assembled inside our runtime — tools, prompts, policies, and guardrails as versioned artifacts.
- Scoped, permissioned tools instead of open-ended system access
- Deterministic checks around non-deterministic steps
- Every artifact reviewed, versioned, and attributable
STAGE 03
Evaluate & harden
Before anything reaches a customer, it faces a battery of tests built from your real cases.
- Golden datasets, regression suites, and adversarial red-teaming
- Accuracy, safety, latency, and cost gates that must be cleared to ship
- Failure modes documented with defined fallbacks
STAGE 04
Deploy with control
Rollout is gradual, observable, and reversible — never a big-bang launch.
- Shadow mode, then canary, then full production
- Human approval gates on high-consequence actions
- One-click rollback and a full audit trail of every release
STAGE 05
Operate & hand over
We run it, watch it, improve it — and progressively transfer ownership to your team.
- Live monitoring, drift detection, and on-call response
- Continuous evaluation against production traffic
- Onboarding, roles, and runbooks so your team can own it
02 / The agent harness
A runtime that treats agent access like a security boundary.
Agents are powerful precisely because they can act. The harness is what makes that safe: every capability is a scoped, permissioned, recorded tool — never raw access to your systems.
Policy engine
Guardrails run before and after every step — PII redaction, allowed-action lists, spend caps, and business-rule checks enforced in code.
Full traceability
Every prompt, tool call, and decision is captured and replayable. You can reconstruct exactly why an agent did what it did.
Human-in-the-loop
High-consequence steps pause for review. Approvals, edits, and overrides are first-class — and themselves logged.
Context engineering
The context window is a finite attention budget. Just-in-time retrieval, compaction, and structured notes keep the high-signal tokens in and the noise out.
03 / Testing & validation
An agent ships when it earns it — and keeps earning it.
We treat agents like the critical software they are. Evaluation is not a launch checkbox; it runs on every change and against live traffic, forever.
01
Golden datasets
Curated from your real historical cases, with verified correct outcomes, so we measure against reality — not vibes.
02
Regression suites in CI
Every prompt, tool, or policy change is re-scored automatically. A regression blocks the release; nothing degrades silently.
03
Adversarial red-teaming
We attack the agent — prompt injection, malformed inputs, edge cases, jailbreaks — and encode every finding as a permanent test.
04
Online evaluation
Sampled production runs are scored continuously by automated judges and human reviewers, with drift and quality alerting.
05
Trajectory evaluation
We score the path an agent takes — tool choice, order, and safety — not just the final answer. Reaching the right answer the wrong way still fails the next case.
06
Threshold gates
Accuracy, safety, latency, and cost must clear agreed bars to promote. The gates are explicit and shared with you.
04 / Deployment & operations
Reproducible releases. Reversible rollouts. Nothing by hand.
The same engineering rigor you'd expect from any production system — applied to agents, where the stakes of an unreviewed change are higher.
Versioned everything
Prompts, tools, models, and policies are versioned artifacts. Any production state can be reproduced exactly and traced to a change and a person.
Staged rollout
Shadow → canary → full with rainbow deployments — both versions run so in-flight agents finish undisrupted. Blast radius is contained at every step.
Instant rollback
A bad release is one action to reverse. Kill-switches can pause an agent or a single capability without a deploy.
Observability
Traces, metrics, cost, and quality on one control plane — emitted with OpenTelemetry GenAI conventions so telemetry is portable and drives cost attribution.
Managed runtime
Agents run on a managed agent runtime — AWS Bedrock AgentCore, Cloudflare, or LangGraph Platform. We deploy our harness onto it rather than reinvent hosting.
05 / Governance, compliance & contracts
Enterprise-grade means audit-ready — on day one, not after an incident.
We build to the controls your legal, security, and compliance teams will ask for, and we put them in writing. The full technical treatment lives in our architecture dossier.
01
Immutable audit logs
Every input, decision, tool call, and human approval is recorded to a tamper-evident trail with defined retention.
02
Access & identity
Role-based access, SSO/SAML, least privilege, and scoped credentials for both people and agents.
03
Data protection
Encryption in transit and at rest, tenant isolation, PII handling and redaction, and clear data-residency options.
04
Framework alignment
Controls mapped to SOC 2, GDPR/CCPA, and the NIST AI Risk Management Framework, with a roadmap to certification.
05
Contracts & ownership
MSA, DPA, SLAs, and explicit terms on who owns models, data, and outputs — and what happens at offboarding.
06 / Team & onboarding
Your team in the driver's seat, not on the sidelines.
Built for your whole team to use — and eventually to own.
The platform isn't a black box we keep to ourselves. It's a workspace your operators, reviewers, and administrators log into, with the controls and training to run agents themselves.
Roles & workspaces
Multi-seat teams with roles — operator, reviewer, admin — and permissions scoped to what each person needs.
Review & approvals
A shared queue where humans approve, edit, or reject agent actions — every decision attributed and logged.
Guided onboarding
Role-based training, runbooks, and in-product guidance designed for operators, not just engineers.
Handover, by design
Ownership transfers on a defined path — from Meridian-run, to co-managed, to fully yours.
See it on your workflow