Give your coding agents a memory, a process and a [i]budget[/i]
PraxentHarness℠ gives your agents the governance, knowledge and engineering practices they’re missing, so they deliver quality features fast and at scale. It makes every engineer on your team an AI power user by default.
AI agents code like very fast junior engineers. Without context or guardrails, they duplicate what already exists, guess at intent and ship features that drift from what you asked for. In financial services, on large existing codebases, that speed turns into tech debt and fragile systems.
PraxentHarness is a governed knowledge hub that lives in your repo. Your engineers approve what agents treat as truth, and every feature follows one five-phase product development lifecycle (PDLC). Specialist agents run each phase with built-in skills, and you see what every story costs.
Agents miss code that already exists and write their own, so duplicates pile up.
One agent invents a rule, writes it down, and the next agent treats it as law.
A one-line ticket goes straight to code, and the misunderstanding surfaces in review.
Token spend arrives as one monthly bill, so fixed-scope work turns into a guess.
Memory
Memory
Memory
Memory
Memory
A hub of rules, rationale and principles your engineers approved, stored in Git next to the code.
Process
Process
Process
Process
Process
Intent, Design, Plan, Build and Ship, with an artifact a person approves at every gate.
Specialist agents
Specialist agents
Specialist agents
Specialist agents
Specialist agents
Each phase runs a defined role, model and set of skills, so reviewers know what every agent was told.
Budget
Budget
Budget
Budget
Budget
Cost per story, acceptance criterion, model and agent role, while you can still change course.
Every feature follows one governed path, with a person approving each [i]step[/i]
Every feature follows one governed path, with a person approving each [i]step[/i]
Every feature follows one governed path, with a person approving each [i]step[/i]
Every feature follows one governed path, with a person approving each [i]step[/i]
Every feature follows one governed path, with a person approving each [i]step[/i]
Start from the codebase you’ve got: in flight, legacy or new
Start from the codebase you’ve got: in flight, legacy or new
Start from the codebase you’ve got: in flight, legacy or new
Start from the codebase you’ve got: in flight, legacy or new
Start from the codebase you’ve got: in flight, legacy or new
Agents land in a repo with no map, so every session rediscovers the system and guesses at the parts it can’t see.
Terraform installs a hub on a project in flight, Harvest mines a legacy app ahead of a rebuild, and Greenfield starts clean. All three end in one hub your team governs and every agent reads first.
Your team signs off on the map before any agent builds on it
Your team signs off on the map before any agent builds on it
Your team signs off on the map before any agent builds on it
Your team signs off on the map before any agent builds on it
Your team signs off on the map before any agent builds on it
Agents infer the system’s boundaries on their own, couple modules that should stay apart and build on structure nobody agreed to.
The harness scans your code and proposes service areas, concepts and principles. Your team agrees, merges or rejects each one, and the hub installs what you agreed.
Every feature starts from the why, approved by a person
Every feature starts from the why, approved by a person
Every feature starts from the why, approved by a person
Every feature starts from the why, approved by a person
Every feature starts from the why, approved by a person
The agent gets a ticket that says what to build. Months later nobody knows why the rule exists, so nobody dares change it.
An interviewer agent draws out the problem and the outcome before anyone proposes a solution. A person approves intent.md, and every agent after reads it.
Every feature moves through Intent, Design, Plan, Build and Ship
Every feature moves through Intent, Design, Plan, Build and Ship
Every feature moves through Intent, Design, Plan, Build and Ship
Every feature moves through Intent, Design, Plan, Build and Ship
Every feature moves through Intent, Design, Plan, Build and Ship
Agents jump from a one-line ticket straight to code, and you find the misunderstanding in code review, after the tokens are spent.
Each PDLC phase produces an artifact a person approves: intent.md, spec.md, plan.md, then code and tests. Approving the artifact moves the card forward.
A specialist agent runs each phase with the skills it needs
A specialist agent runs each phase with the skills it needs
A specialist agent runs each phase with the skills it needs
A specialist agent runs each phase with the skills it needs
A specialist agent runs each phase with the skills it needs
One general agent does everything with whatever context it grabbed, so quality swings from session to session.
Workflows give every step a role, a model, an effort level, its skills and the hub files it must read. Seven agents run brief to ship, and a person approves every phase.
What agents learn becomes truth only when a person approves it
What agents learn becomes truth only when a person approves it
What agents learn becomes truth only when a person approves it
What agents learn becomes truth only when a person approves it
What agents learn becomes truth only when a person approves it
Agents write invented reasoning into the docs. The next agent reads it as law, and you can’t tell what was decided from what was imagined.
Every change to the hub lands as a draft with a diff and the agent’s reasoning. A reviewer approves it on the pull request, and Git keeps an audit trail.
Principles tell agents the goal, the reason and when to deviate
Principles tell agents the goal, the reason and when to deviate
Principles tell agents the goal, the reason and when to deviate
Principles tell agents the goal, the reason and when to deviate
Principles tell agents the goal, the reason and when to deviate
A rigid rule fires in a case nobody anticipated, and nothing catches it until production does.
Each principle carries the goal, rationale and escape clause your engineers set, plus the reviewer agent or lint rule that enforces it on every change.
Know what every story costs while you can still change course
Know what every story costs while you can still change course
Know what every story costs while you can still change course
Know what every story costs while you can still change course
Know what every story costs while you can still change course
Token spend shows up as one monthly bill, so a fixed-scope project turns into a guess.
You see cost per story, per acceptance criterion, per model and per agent role, and what each hub file costs to read, in time to change scope or swap a model.
Lives in your repo
Lives in your repo
Lives in your repo
Lives in your repo
Lives in your repo
The hub is versioned in Git next to your code, with no server to run.
Open format
Open format
Open format
Open format
Open format
Built on Google’s Open Knowledge Format, so every file is plain Markdown your team can read.
Built for Claude Code
Built for Claude Code
Built for Claude Code
Built for Claude Code
Built for Claude Code
Agents, workflows and skills are installed per project and tracked by usage and cost.
Praxent is an award-winning AI transformation firm built exclusively for financial services firms that want to change the way they do business.
Make your whole engineering team AI power users
Make your whole engineering team AI power users
Make your whole engineering team AI power users
Make your whole engineering team AI power users
Make your whole engineering team AI power users
Praxent installs PraxentHarness with your engineers as part of Claude Code enablement for engineering and product teams. We drive adoption across the department and move your team from AI as autocomplete to loop engineering, where agents build features from intent to ship without adding tech debt or fragility.