AI Engineering Infrastructure
Production infrastructure for agentic development
AI coding agents are powerful. But running them without session tracking, conformance enforcement, and observability is like deploying microservices without monitoring — it works until it doesn't, and you can't debug why.
We build the infrastructure layer that makes agentic development reliable at scale: session telemetry, machine-readable specifications, automated conformance checks, and real-time observability. Your agents, governed by your standards, visible in your dashboards.
The problem
Agents are easy to start. Hard to operate.
Getting an AI agent to write code takes minutes. Getting it to write code that consistently matches your architecture, follows your conventions, and doesn't introduce drift across a multi-repo ecosystem — that's an infrastructure problem. And most teams are solving it with willpower instead of systems.
Agents without infrastructure
AI coding agents can generate code. But without session tracking, conformance enforcement, and observability, you have no idea whether they’re producing code that matches your architecture — or slowly introducing drift across your codebase. Most teams discover the problems in production.
No visibility into agent behaviour
Your agents are running sessions, reading files, making decisions. But you can’t see what they did, why they did it, or whether it worked. Without structured session data and telemetry, debugging an agent’s output means reading diffs and guessing at intent.
Standards that exist on paper only
You have coding standards, architecture decisions, and review guidelines. But AI agents can’t read your team’s wiki. Without machine-readable specifications and automated conformance checking, every agent session starts from zero context about how your team builds software.
Scaling agents without losing control
Running one AI agent is manageable. Running a fleet of them across multiple repositories — with budget controls, health monitoring, and coordinated workflows — is an infrastructure problem. Most teams hit this wall when they try to move past the single-developer, single-repo stage.
What we build
The infrastructure layer between your agents and your codebase
We've built and operate this infrastructure ourselves — across 15 repositories with automated agent fleets. These aren't theoretical patterns. They're systems we run in production, refined through thousands of agent sessions.
Context architecture & conformance
Machine-readable standards that agents check before they write code
Most teams give AI agents a README and hope for the best. We build a two-layer system: structured context files that tell agents how your team builds software, and executable specifications that verify agents actually followed the rules.
Every agent session starts with a conformance check. Before writing a single line of code, the agent knows whether the repository is READY or has VIOLATIONS — and gets machine-readable remediation steps it can act on directly.
- CLAUDE.md hierarchy: Root and directory-level context files encoding your architecture, patterns, constraints, and conventions — loaded automatically with every session
- Executable specifications: Typed spec definitions with automated checks — compiler strictness, import hygiene, test structure, dependency pinning — not wiki pages, but code that runs
- Session-start hooks: Pre-flight conformance checks that report READY/VIOLATIONS before the agent begins work, with structured remediation guidance the agent can follow
- Cross-repo conformance matrix: A single view of spec compliance across your entire ecosystem — which repos conform, which are drifting, and what needs attention
What this looks like in your codebase:
Session tracking & fleet orchestration
See what your agents are doing, and manage them at scale
Running one AI agent is manageable. Running a fleet of them across multiple repositories — knowing which sessions succeeded, which failed, which are idle, and how much they cost — requires real infrastructure. We build the data pipeline and orchestration layer that makes this visible and controllable.
Session data pipeline
Every agent session is captured: files read, tools used, tokens consumed, outcomes. Ingested into PostgreSQL via a filesystem watcher with content-hash deduplication and incremental sync.
Terminal state parsing
Real-time parsing of agent terminal output into structured state — idle, generating, waiting for approval, error — using a state machine tested against hundreds of live-captured fixtures.
Fleet lifecycle management
Start, stop, monitor, and recycle agent sessions across repositories. Idle detection with two-strike thresholds, context-percent recycling, and thundering-herd jitter for safe restarts.
Budget and provider health
Track AI API costs against budgets, monitor provider rate limits, and surface health status per provider — so you know before your agents hit a wall.
Query and debug interface
A CLI for querying session history, searching across sessions, and analysing agent behaviour — so debugging an agent’s output starts with data, not guesswork.
Periodic self-validation
The session pipeline validates itself: a full sync runs periodically and treats any newly-inserted records as evidence the live pipeline missed events. Missed events trigger alerts.
Built on real infrastructure we operate across our own 15-repository ecosystem — session-ledger alone processes thousands of agent sessions with full telemetry.
Observability & efficiency measurement
Dashboards, alerts, and analysis that tell you whether your agents are actually working
Session tracking tells you what happened. Observability tells you whether it's working. We build a full telemetry stack — dashboards, alerts, and agent efficiency analysis — so you can quantify whether your AI investment is paying off and catch problems before they compound.
- Dashboard-as-code: Grafana dashboards defined in TypeScript and compiled to JSON — covering agent health, session quality, cost tracking, conformance drift, and infrastructure status
- Agent efficiency scoring: Classify agent tool calls into exploration vs production phases, score navigation against your dependency graph, and measure how efficiently agents traverse your codebase
- Conformance drift detection: Track spec compliance over time across your ecosystem — which repos are converging toward standards, which are drifting, and alert on regressions
- Self-monitoring observability: The observability stack monitors itself: detecting stale queries, orphaned log streams, alerts that have never fired, and computing signal-to-noise ratios across your telemetry
The result: you can answer questions like “how much did our agents cost this week?”, “which repos are drifting from our standards?”, and “are agents getting more efficient at navigating our codebase over time?”
This is what separates teams running agents from teams operating them.
How it works
Four phases. Infrastructure you keep. No ongoing dependency.
We don't run workshops and leave. We build real infrastructure in your environment — session tracking, conformance checks, dashboards, specifications — tested against your actual codebase and running before we hand it over.
01
Assess: Audit your codebase and agent readiness
Week 1
- Map your repository structure, architecture patterns, and existing CI/CD pipeline to identify where agentic development has the highest leverage
- Evaluate your team’s current AI usage — tooling, workflows, prompt patterns, and where agents are producing inconsistent output
- Baseline your codebase against machine-readable specifications: compiler strictness, import hygiene, test structure, and deployment conventions
- Identify cross-repository dependencies and coordination risks that will matter when agents work across your ecosystem
- Deliver a concrete assessment with prioritised recommendations your team can act on — with or without us
02
Instrument: Build the observability and conformance layer
Weeks 2–3
- Deploy session tracking infrastructure that captures what your agents do — files read, tools used, tokens consumed, and session outcomes — ingested into a queryable database
- Write machine-readable specifications for your architecture standards and wire them into automated conformance checks that run on every agent session
- Build Grafana dashboards and alerts for agent health, session quality, cost tracking, and conformance drift across your repositories
- Configure structured context files (CLAUDE.md hierarchy) so agents understand your architecture, patterns, and constraints before they write a line of code
- Set up budget controls and provider health monitoring for your AI API usage
03
Embed: Run agents on real work with your team
Weeks 4–6
- Pair with your developers on real tickets — running agents against your actual backlog with the conformance and observability layer in place
- Train your team to read session telemetry, diagnose agent behaviour, and iterate on context configuration based on real data
- Refine specifications based on what the agents actually produce — tightening constraints where output drifts, relaxing where they’re unnecessary
- Build agent workflow patterns specific to your team: feature development, bug triage, migrations, and cross-repo coordination
- Scale from single-agent sessions to multi-agent workflows with proper orchestration and monitoring
04
Measure: Quantify and hand over
Week 7–8
- Before/after comparison using real data from the session tracking pipeline: cycle time, conformance rates, agent efficiency scores, and cost per session
- Agent navigation efficiency analysis — how well your agents traverse your codebase, measured against the dependency graph
- Executive summary with concrete metrics your leadership team can use to justify continued investment
- Hand over all infrastructure: dashboards, specifications, session tracking, conformance checks, and context configuration — running in your environment
- Sustainability guide: how to maintain specifications, onboard new repositories, and evolve the system as your codebase changes
Illustrative dashboard — your engagement tracks metrics specific to your team:
Dashboard
AI Engineering Impact — 6 Week Engagement
PR Cycle Time
Deploy Frequency
AI Tool Adoption
Test Coverage
PR Cycle Time (hours) — 6 Weeks
AI Tool Usage by Team Member
In practice
What governed agent development looks like
Not slides. Not architecture diagrams. These are the artifacts that come out of a governed agent workflow — structured PRs from conformance-checked sessions, with full test coverage and traceable agent decisions.
Fleet monitor & conformance matrix
Real-time agent status and cross-repo spec compliance at a glance
The babysitter process monitors every agent session in your fleet via terminal state parsing — detecting active work, idle agents, errors, and high-context sessions that need recycling.
The conformance matrix gives you a single view of spec compliance across your entire ecosystem. Which repos are clean, which are drifting, and exactly which specs need attention.
A governed agent session
Conformance check → claim work → implement → quality gates → advance phase
This is what an autonomous agent session looks like with the infrastructure layer in place. The agent starts with conformance checks, claims work from a queue, implements within the guardrails set by your specifications, passes quality gates, and advances the work item through a tracked phase pipeline.
The babysitter monitors each session via terminal state parsing — detecting idle agents, recycling high-context sessions, and assigning new work automatically. Every action is captured by the session tracking pipeline.
The governed agent lifecycle
Deliverables
Infrastructure your team keeps and operates
Every engagement produces running systems in your environment. Session tracking pipelines, conformance checks, dashboards, and specifications — not documents, but infrastructure that operates continuously after we leave.
Session tracking pipeline
A data pipeline that captures everything your AI agents do — files read, tools used, tokens consumed, session outcomes — ingested into a queryable database with deduplication and incremental sync.
Includes
- Filesystem watcher with reliable event ingestion
- PostgreSQL schema for session data with content-hash dedup
- Query CLI for session analysis and debugging
- Periodic self-validation that detects missed events
Machine-readable specifications
Your architecture standards, coding conventions, and constraints encoded as executable specs that agents check automatically — not documents that gather dust.
Includes
- Typed specification definitions with automated conformance checks
- Session-start hooks that report READY/VIOLATIONS before agents begin work
- Cross-repository conformance matrix
- Remediation guidance agents can act on directly
Observability stack
Grafana dashboards, alerts, and telemetry covering agent health, session quality, cost tracking, and conformance drift — including self-monitoring that detects when observability itself goes stale.
Includes
- Dashboard-as-code definitions compiled to Grafana JSON
- Alert rules for agent health, latency, cost, and conformance
- OpenTelemetry instrumentation across the agent pipeline
- Signal-noise analysis that identifies orphaned queries and silent alerts
Context architecture
Structured CLAUDE.md configuration hierarchy that gives agents deep understanding of your codebase — architecture, patterns, constraints, and team conventions.
Includes
- Root and directory-level context files for each major module
- Constraint rules (security boundaries, data handling, multi-tenancy)
- Pattern documentation pulled from your actual codebase
- Hooks and automation for context maintenance
Agent efficiency measurement
Tooling that analyses how well agents navigate your codebase — classifying tool calls into exploration vs production phases and scoring navigation against your dependency graph.
Includes
- Codebase knowledge graphs built from your TypeScript imports
- Session pathfinding analysis with multi-dimensional quality scores
- Cross-repo dependency tracking and impact analysis
- Before/after efficiency comparison across the engagement
Reproducible environments
Nix-based development environments that ensure every agent and every developer runs against the same toolchain, dependencies, and configuration — eliminating works-on-my-machine failures.
Includes
- Devenv configuration with shared base modules
- Deterministic flake management CLI
- Pre-commit hooks wired to conformance checks
- Environment bootstrap that works for new repos in minutes
Work with Stacktrace
Ready to run your agents on real infrastructure?
We build session tracking, conformance enforcement, and observability for your agentic development workflow. Running in your environment within weeks.
Based in Brisbane. Building agentic infrastructure for engineering teams across Australia and New Zealand.
Contact us today