What has to be true before an agent runs in production.
Ten implementation patterns, each with the failure modes it exists to prevent and the checks that prove it holds. Every pattern here is applied in at least one of the project pages below.
Patterns
Acceptance tests for agent work →
Define done before the agent starts.
How to write acceptance tests that decide whether an agent run shipped something correct, rather than something plausible.
Evals that gate releases →
A golden suite in CI, not a spreadsheet.
Running an adversarial eval harness against a golden task suite as a release gate, with published pass thresholds.
MCP permission scoping →
Deny by default, declare every tool.
Scoping a Model Context Protocol server so a caller reaches only what it declared, with read-only hints and no ambient credentials.
Observability for agent runs →
One trace ID from request to receipt.
Instrumenting agent runs so a failure can be reconstructed without asking the model what happened.
Cost controls that actually stop spend →
Ceilings enforced before the call, not after the invoice.
Per-run, per-agent, and per-caller budget ceilings enforced in the execution path.
Approval gates that suspend the run →
Nothing downstream executes before a human approves.
Human-in-the-loop gates implemented as run suspension rather than as a notification.
Trace replay and diffing →
Replay the run, diff the divergence.
Replaying a recorded run deterministically and diffing two runs to find the first point of divergence.
Recovery and durable checkpoints →
A crash resumes; it does not restart.
Checkpointing long agent runs so an interruption costs one step rather than the whole run.
Rollback for agent-made changes →
Every write has a documented reverse.
Making agent-authored changes reversible: change sets, inverse operations, and a rehearsed rollback path.
Ownership handoff →
The team operates it after the engagement ends.
What has to exist before an AI system can be handed to an internal team: runbooks, dashboards, named owner, and a decommission path.
Where these are implemented
ClawKeeper →
Governed Claude agent control plane. A deterministic policy engine runs before every LLM call, autonomous spend defaults to zero, and the audit log is enforced by the database.
TechTide Harness Kit →
Trust contracts for agent skills expressed as JSON Schema: risk tiers, tool permissions, deny-by-default egress, approval gates, and prompt-injection fixtures behind a wall of CI validation gates.
CipherClaw →
Deterministic multi-agent debugger with zero LLM calls in the analysis path: causal graphs, weighted root-cause scoring, and failure prediction.
Swarm 357 →
357 Claude agent roles across six business layers, with portable memory, enforced cost caps, a bash policy gate, and durable checkpoints.
FintheFinder →
Deep-research agent whose runs suspend until a human approves, with a deterministic citation audit that keeps AI out of the verification path.
TechTideAI2 →
Company-scale agent platform on a three-plane control/data/evidence architecture, with state-machine transitions, hard execution timeouts, and an adversarial eval harness over a 33-task golden suite.
BRI →
Video intelligence service with a Streamlit operator surface, a FastAPI MCP server, SQLite durability, and multimodal tooling.
AgentRewind →
Trace forensics build: zero-trust import parser and a diff engine for replaying agent runs. Private build with no public artifacts.
Molten →
Governed agent runtime: kill switch, circuit breakers, four-tier sandbox, dead-man's switch. Private build with no public artifacts.
Every pattern in this track
- Acceptance tests for AI agents: define done before the run
- LLM evals in CI: a golden suite that gates releases
- MCP server permissions: deny by default, declare every tool
- AI agent observability: one trace ID from request to receipt
- LLM cost controls: budget ceilings enforced before the call
- Human approval gates for AI agents
- Agent trace replay and diffing
- Durable checkpoints and recovery for agent runs
- Rollback for agent-made changes
- Handing over an AI system your team can operate
Engagement scope, process, and pricing live on TechTide AI. This domain only carries the technical proof.
AI Production Systems at TechTide AI→© 2026 Alex Cinovoj · TechTide AI · Columbus, OH