Technology / Agentic Systems
Anyone can claim an agent does something. Almost no one can show you what it actually does, how reliably, and under what conditions. That gap is where enterprises stop deploying.
Toolformer (Schick et al., 2023) showed language models can learn to call external tools. ReAct (Yao et al., 2023) gave a structure to reasoning-and-acting workflows. The 2024 surveys describe a field where agents specialise, collaborate and solve real tasks.
What no one has solved at scale: how do we verify what an agent can actually do, how reliably, and under what conditions? That is the work.
A useful agentic system is not a collection of prompts behind a dispatcher.
It needs role design, tool boundaries, memory permissions, retrieval policies, escalation rules, evaluation frameworks, audit logs, and human-in-the-loop checkpoints. These are first-class architectural concerns. Treating them as afterthoughts is how prototypes become liabilities.
Every system we ship defines, explicitly:
Typed, scoped, with explicit task boundaries.
Per-agent access control. Not blanket capability.
Read/write rules on the Knowledge Object layer.
Which retrieval pipeline each agent uses for which task.
Orchestration, swarm, hierarchical — chosen by problem class.
Explicit conditions that route to human review.
Capability benchmarks gated behind minimum sample sizes.
Every action, tool call and reasoning step, loggable by default.
Outputs checked against schema and against KO provenance.
Every framework promises autonomous agents. Almost no enterprise can deploy them, because no one can answer the only question that matters: what does this agent actually do, how reliably, and under what conditions? AgentStreet is our marketplace for production-ready, capability-verified agents — and the infrastructure that makes those words mean something.
01
The Agent Card spec. Capabilities, tools, tested reliability, model independence, escalation rules — one contract every agent publishes. No more black boxes hiding behind a chat UI.
02
Shared evaluation suites per vertical. Capability claims gated behind minimum sample sizes, with early-data labelled as such. Reliability is measured, not asserted.
03
Sandboxed execution with full audit logs of every tool call and reasoning step. Agents are exercised against canonical task batteries before they reach a buyer.
04
Peer review, issue tracking, contribution flows. Agents are built by many and audited by all. The community is part of the verification mechanism — not a marketing layer.
05
Architectural patterns codified. Role design, tool boundaries, retrieval policies, memory permissions, escalation. The thing that turns prompt engineering back into engineering.
06
Agents are tested across providers — GPT, Claude, Gemini, open-source. Swap cost is measured and published. Model lock-in is rejected at the spec level, not as an afterthought.
Capabilities are claims. Tested reliability is evidence. Tools are scoped. Model independence is measured, not promised. The card is the only artifact a buyer needs before integrating an agent.
agent-card.yaml
agent:
name: pe-intelligence-agent
version: 0.4.2
category: investment-intelligence
capabilities:
- id: deal-comparable-analysis
description: Build comparables for a target company
tested_on:
n_samples: 142
success_rate: 0.91
ci_95: [0.86, 0.95]
label: validated # not "early data"
tools:
- market-intelligence-search
- financial-data-api
- knowledge-base-read # scoped, read-only
model_independence:
tested_with: [gpt-5, claude-opus-4-7, gemini-3-pro]
swap_overhead_pct: 3.2
audit:
log: [tool_call, reasoning_step, output]
human_in_loop_on: [output_publish, escalation]Boutique curated catalog first — Research, Marketing, Venture verticals — open marketplace second. The six pillars together are what makes agentic structures LLM-agnostic by construction, not by hope.
Each one exercises the same architectural discipline at a different point on the autonomy spectrum.
Research instrument
Autonomous scientific hypothesis generation. Magellan reads across silos and proposes mechanistic connections no single researcher would have found — then evaluates them on novelty, plausibility and falsifiability. Test framework execution phase, expert validation next.
Codebase modernisation
A multi-agent system for understanding, refactoring and modernising legacy enterprise codebases. Specialised agents handle dependency mapping, architectural analysis, refactoring planning, test generation, migration — coordinated through a shared knowledge layer.
Enterprise software builder
Builds enterprise software from specifications. Agents for requirements, architecture, implementation, testing, deployment — with human review gates at every architectural decision point. Ready for commercial transition.
AgentStreet verticals
The Madara stack, decomposed for AgentStreet: Startup Evaluator (automated diligence on early-stage companies), PE Intelligence (deal intelligence for PE workflows), Portfolio Monitor (continuous monitoring of portfolio company signals).
Agentic media intelligence
Multi-agent analysis monitoring media actors, extracting claims, mapping narratives and comparing framing across outlets. Specialised agents for ingestion, claim extraction, clustering, framing analysis. Outputs that respect what the analyst is trying to do.
Architectural pattern
When one model pass is not enough: one layer retrieves and structures evidence, another classifies, another tests for contradictions, another generates output, another reviews quality. Used in investment analysis, compliance, research review, AI transformation work.
If you are evaluating agent frameworks, building internal agentic workflows, or trying to take a prototype past the demo stage — we run architecture reviews and capability benchmarks.