Agent Engineering
Roughly two thirds of enterprise agent failures trace to context drift rather than model capability. Teams answer by buying a stronger model, and the failures continue. We work on the layer around the model.
Architecture decides the ceiling. Memory, retrieval, tool design, orchestration and observability are chosen once and lived with for years, so they are worth choosing on evidence rather than on whichever vendor published first.
Vendor neutral
We resell none of the tools we recommend, so the advice follows your constraints.
Evidence led
Every recommendation traces back to a measurement you can rerun yourself.
Yours to keep
The evaluation sets built during the engagement are delivered to your team.
Your product
the thing users actually asked for
Context and memory
context, memory, search, graph
Multi-agent systems
loop, agentic, code
Evals and guardrails
eval, safety, red teaming
Model and runtime
swappable, and rarely the reason a system fails
Fit
Who This Is For
Teams building or rebuilding an agent system, where architecture decisions get made once and lived with for years.
Platform teams
Groups who rolled out coding agents and cannot explain the uneven results
Engineering leaders
Anyone asked to justify agent spend without a measurement to point at
AI product teams
Products that work in a demo and drift once real traffic arrives
Large codebases
Repositories too big for an agent to hold in a single context window
Process
How It Works
Architecture review
We map what exists, what it has to do, and the constraints around retention, access and compliance that narrow the options
Build the evaluation
Tasks drawn from your repository and the failures your team keeps hitting, turned into a set you can rerun forever
Measure and improve
A baseline first, then candidate configurations compared against it, with a gate that fails anything breaking solved work
Adopt or roll back
You receive the recommended configuration, the evidence behind it, and a documented path back if it disappoints
Deliverables
What You Receive
Stack scorecard
Every layer scored, with findings ranked by severity and each one tied to the change that addresses it
Evaluation set
Tasks built from your own code and your own failures, delivered to your team to keep and rerun
Benchmark package
Baseline against candidates on fixed tasks and a fixed model, with the raw runs included
Memory decision
A written architecture choice covering persistence, retention and access, with the reasoning attached
In practice
What an Engagement Looks Like
Three shapes this work usually takes. Scope is agreed before anything starts, and none of these depend on you running a particular vendor.
Architecture review first
A written assessment of the layers you have, ranked by severity, with each finding tied to the change that addresses it.
Evaluation before rebuild
Task sets drawn from your repository and your own failures, so the rebuild that follows can be judged rather than argued about.
Built alongside your team
Implementation done in your stack with your engineers present, because the architecture has to survive after handover.
Pricing
Engagement Options
Agent engineering spans twelve disciplines: prompt, context, harness, memory, inference, eval, loop, agentic, code, protocol, graph and search. These three columns group them into work you can scope and buy. The discipline itself is set out at agentengineering.world, which we maintain.
Most requested
Context & Memory
What the agent knows
Typically 4 to 8 weeks
Architecture and build
Context assembly, memory that survives a session, retrieval and the search behind it.
- Context and memory engineering
- Retrieval, search and graph design
- Persistence and retention policy
- Compaction and window strategy
- Implementation into your stack
- Written decision record
Multi-Agent Systems
How the work is divided
Typically 6 to 8 weeks
Design and orchestration
Roles, handoffs, loops and recovery for systems where several agents share the work.
- Loop and agentic engineering
- Code engineering and tool surfaces
- Harness engineering and control flow
- Recursive routing for large codebases
- Failure and recovery behaviour
- Observability across the system
Evals & Guardrails
How you know it holds
Typically 4 to 6 weeks
Measurement and safety
Evaluation designed alongside the system, with guardrails and adversarial testing behind it.
- Eval engineering per role
- Task sets from your own workload
- Guardrail and permission design
- Adversarial and red-team testing
- Observability and tracing
- Handover to your engineering team
Prices exclude VAT where applicable.
People
Who You Work With
Work is delivered by the Superagentic AI team, supported by consultants and researchers brought in on demand when an engagement calls for depth in a specific area. The same lead stays with you from the first call through to handover, so context does not get rebuilt between phases.
Open source
Projects Behind This Work
Every discipline below is one we build in the open. You are not buying these tools, and we work in whatever stack you already run.
SpecMem
Unified agent experience and cognitive memory, readable by any coding agent.
AgentVectorDB
Vector database built for agent memory and retrieval, with cognitive layers.
PyFlue
Python-native agent harness framework, ported from Flue.
SuperClaw
Scenario-driven security testing to red-team agents before deployment.
SuperOptiX
Full stack agentic framework spanning the major agent runtimes.
Agent Engineering
The manifesto and the twelve disciplines this practice is built on.
Questions
Frequently Asked Questions
Ready to improve your agent architecture
Tell us what you are building and the constraints around it. We will come back with scope.
Working from London and San Francisco. Remote by default, on site where it helps.